{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical 7: Unsupervised Learning \n",
    "\n",
    "\n",
    "Upon completion of this session you should be able to:\n",
    "- understand how K-Means, Hierarchical Clustering and Density-based Clustering algorithms work.\n",
    "- be able to apply these unsupervised learning algorithms in Python.\n",
    "\n",
    "---\n",
    "- Materials in this module include resources collected from various open-source online repositories.\n",
    "- Jupyter source file can be downloaded from https://github.com/gaoshangdeakin/SIT384-Jupyter\n",
    "- If you found any issue/bug for this document, please submit an issue at [https://github.com/gaoshangdeakin/SIT384/issues](https://github.com/gaoshangdeakin/SIT384/issues)\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "This practical session will demonstrate different unsupervised learning algorithms: K-Means, Hierarchical Clustering and Density-based Clustering.\n",
    "\n",
    "\n",
    "## Background\n",
    "\n",
    "\n",
    "\n",
    "### Part 1 K-Means\n",
    "\n",
    "1.1 [Generating Random Data](#data)\n",
    "\n",
    "1.2 [Setting Up K-means](#kmeans)\n",
    "\n",
    "1.3 [Creating the Visual Plot](#plot)\n",
    "\n",
    "1.4 [Clustering Iris Data](#iris)\n",
    "\n",
    "\n",
    "### Part 2 Hierarchical Clustering\n",
    "\n",
    "2.1 [Generating Random Data](#data2)\n",
    "\n",
    "2.2 [Agglomerative Clustering](#agc)\n",
    "\n",
    "2.3 [Dendrogram](#den)\n",
    " \n",
    "### Part 3 Density-based Clustering\n",
    "\n",
    "## Tasks\n",
    "\n",
    "## Summary\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## <span style=\"color:#0b486b\">1. K-Means Clustering</span>\n",
    "\n",
    "\n",
    "### Import the following libraries:\n",
    "<ul>\n",
    "    <li> <b>random</b> </li>\n",
    "    <li> <b>numpy as np</b> </li>\n",
    "    <li> <b>matplotlib.pyplot as plt</b> </li>\n",
    "    <li> <b>KMeans from sklearn.cluster</b> </li>\n",
    "    <li> <b>make_blobs from sklearn.datasets.samples_generator</b> </li>\n",
    "</ul>\n",
    "<br>\n",
    "Also run <b> %matplotlib inline </b> since we will be plotting in this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.cluster import KMeans \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"data\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.1 Generating Random Data</span>\n",
    "\n",
    "So we will be creating our own dataset!\n",
    "\n",
    "First we need to set up a random seed. Use <b>numpy's random.seed()</b> function, where the seed will be set to <b>0</b> <br>ex. random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will be making <i> random clusters </i> of points by using the <b> make_blobs </b> class. The <b> make_blobs </b> class can take in many inputs, but we will be using these specific ones. <br> <br>\n",
    "<b> <u> Input </u> </b>\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Value will be: 5000 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Value will be: [[4, 4], [-2, -1], [2, -3],[1,1]] </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. </li>\n",
    "    <ul> <li> Value will be: 0.9 </li> </ul>\n",
    "</ul>\n",
    "<br>\n",
    "<b> <u> Output </u> </b>\n",
    "<ul>\n",
    "    <li> <b>X</b>: Array of shape [n_samples, n_features]. (Feature Matrix)</li>\n",
    "    <ul> <li> The generated samples. </li> </ul> \n",
    "    <li> <b>y</b>: Array of shape [n_samples]. (Response Vector)</li>\n",
    "    <ul> <li> The integer labels for cluster membership of each sample. </li> </ul>\n",
    "</ul>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the scatter plot of the randomly generated data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29ff7a88a88>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], marker='.')\n",
    "\n",
    "#for testing purposes\n",
    "#fig,ax = plt.subplots(figsize=(7, 7), dpi=100)\n",
    "#ax.plot(X[:, 0], X[:, 1], 'k',\n",
    "#            markerfacecolor=col, marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a id = \"kmeans\"></a>\n",
    "\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.2 Setting up K-means</span>\n",
    "\n",
    "Now that we have our random data, let's set up our K-Means Clustering.\n",
    "\n",
    "The KMeans class has many parameters that can be used, but we will be using these three:\n",
    "<ul>\n",
    "    <li> <b>init</b>: Initialization method of the centroids. </li>\n",
    "    <ul>\n",
    "        <li> Value will be: \"k-means++\" </li>\n",
    "        <li> k-means++: Selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.</li>\n",
    "    </ul>\n",
    "    <li> <b>n_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
    "    <ul> <li> Value will be: 4 (since we have 4 centers)</li> </ul>\n",
    "    <li> <b>n_init</b>: Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. </li>\n",
    "    <ul> <li> Value will be: 12 </li> </ul>\n",
    "</ul>\n",
    "\n",
    "Initialize KMeans with these parameters, where the output parameter is called <b>k_means</b>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_means = KMeans(init = \"k-means++\", n_clusters = 4, n_init = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's fit the KMeans model with the feature matrix we created above, <b> X </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=4, n_init=12, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's grab the labels for each point in the model using KMeans' <b> .labels\\_ </b> attribute and save it as <b> k_means_labels </b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 3 3 ... 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "k_means_labels = k_means.labels_\n",
    "print(k_means_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also get the coordinates of the cluster centers using KMeans' <b> .cluster&#95;centers&#95; </b> and save it as <b> k_means_cluster_centers </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.03743147 -0.99782524]\n",
      " [ 3.97334234  3.98758687]\n",
      " [ 0.96900523  0.98370298]\n",
      " [ 1.99741008 -3.01666822]]\n"
     ]
    }
   ],
   "source": [
    "k_means_cluster_centers = k_means.cluster_centers_\n",
    "print(k_means_cluster_centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a id = \"plot\"></a>\n",
    "\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.3 Creating the Visual Plot</span>\n",
    "\n",
    "So now that we have the random data generated and the KMeans model initialized, let's plot them and see what it looks like!\n",
    "\n",
    "Please read through the code and comments to understand how to plot the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tGwKBQLNgxAsd7FVo2aipqYEaDzsiWjMSNsE5ZkSjZgFFIlCEhBa3AHGERa+BYgGQ9mhXZuegqYxPjjcxgOdFG+Fa1h/CiKr10hUG7aBZ3lcz/LKNFik9uOSNXyNbRJ7tuH6cj85EQolAPbATBw4cAADs3bsX8+bNw8svv4zPP/8cANMnDxgwwPY5jRgxAgAwZcqUI/0d8QDYc88Pl8+bN88CVnbqwB1QMsWjOwMdN7lXMniZFEQMiXij/TiaZkS0qpks8+caIFxkUAgOqZkcNBJvVlphJ/SK1SYdoETYdoY07WDGgDJqRtou1yRpG/6eOdXhdj0aSm1cbkJTkZ+fj9tuuw033HAD5GhQlFa//fbbzcrXtm3bhtzc3CP9HfEA2HPPD4f7fD7oStQCVkyBMG/ePDz33HM2QGlsbEwCETt4KXYwtETKAozyZppFFZrKtuGgal3XppBgnLEdsA0Ko3q9HfR1jUXH1vMMVhuvW6NfQxnB+WTjvSf8eYzeEIoJ49zEebpEzC7RdFFREdMGi2UsAtaN/ZWVlWHlypXYvXu3Dajdms5bferUqQCAiy666Eh+TzwA9tzzg/mPEeunpaVBrVgDZ+KsuroaPp8P1157rXiNN+FhLR5dgKihFAn/Zva479/ComoRGRqRZUNpErjqxRlINJSwIg6j1JgpJLhiIQsJTXGAHo+U5SRpWcKfh4QStgBrxARTB00gzrFqLTs33jdCU5EIVjOAt74XXlnXAkfcXIMf89zlpO0TarxVVXQ9evRAOBzG/Pnzj+R3ywNgzz1vyXkprFaZCTkaxOOPP46zzz4bnTt3FuuMGjUqCaBZj4d6KGVrEIs0IT8/3wYWr7/+upgo/Pvf/x6TJ0/GqlWr8Oijj7oDi6XklwGNCVh63kwkgntc1AsmICU0VfC+Jo0QQiJY7TgWj06jSDSVQw/sFFGy4FvjjaZcTdAEeZYIOGpL7LHteEQchrWaTrwXB+/NbdasWS2Cru3cHbTOSy+9hNGjR7f65rlgwQKEQiF07979SH2/PAD23POWPC0tDVplJpzAoOs6Kisr8cMPPwiwUWIh3HXXXejatSuIkiPnMWPGYP369a7gocRCzUZ/jz32WPOgI+gIGc5WkbaiCq5saCgxZGRWKsDgcnXdVFDw4g1LBAw4+GauurBSHJFaBrKBIoOeUNm//lyYNwYt+T1Yypx5g/e2mpWyeOONN2BU6bbaL7nkEgDA/ffff6S+X14psmeetWQ+n4/Wfv0ldW7YRimDLqWyymp6/vnn6aSTTqLTTjuNxo4dSyellBNK/20r7fX7/VRSUiK8tLRU/P+UU06hLVu22I6DPdliH+GeF1L37t1ty2fOnEmPPfaYub6mEkEl8ucQDRxJJKWyEuMD+SSdeiNR95OIoBDJEaJOXYni9URdehH5NxEN8BGldmFlxpEakroOIup9DhFJRAQi6EQRP1Goip3T2f9H1Pd8Bhk13xrneRNRz9OJju1LBImIdFEeTcEKom6DicJ7iY4bSlS3naj3MFbi3OssIqmDraQY+7cQihaSdPYkkvpfYLw/Oak823a9DrKciKixsZFyc3MpJyeHNm/eTDk5OVRTU9PiSCT+uVx44YWt+HYcsnmlyJ57bnU3vpe/Nnv2bACsSKJHjx5iWSRYD7UmB7qDT5VlWYwIam30pskRIaNKXscZ4W6G4E+De5KSayIC9edBbyy3POIbnK2IiONIRAPm+s6knjVStlIUSthIGjoq73SdqSiMCrmEEmHnEw3Y1A829YWhdBDv1dif7s9zvxYWuoFTOYsXL0aXLl1w7rnn4u6778acOXOQm5srlgOMR1bj4WZ54QceeAAAcMkllxyJ75tHQXjuOXefz4eY4HtDSEtLw5lnnmmTLt15552QZRkFBQU44YQTQESYPHkylFgIWmUmVDm5Uqs5CwaDrtVf5mO9YlcWBKsh+vdWZSGhW3TDvGeDpiARrnEBzUxTEaGEksuNuXLCKhdTZQGm4pjhWnYMrpbg/K/1xhAodgFljSX4DtJJDbDcaPh++E3A+Jetk6yCcALqKaecgrvvvhtLliyxTf3gvTeUsmRlRPfu3REKhbBgwYIj8Z3zANhzz7kz5QIHOBMU4nE2wPKjjz7CtGnTRGKopqYGw4cPb3a7g5m1qo1pezngZjKQtCkWjKSbtQqNA1OgmG0bb2Ra3WigGZ2sAYTB6uSEXbAaCV2FHm9g+2naY5eL8WMJgHXIyXjEzaPswE7H9rlJ/LY1AWd7vSqLKTj4jcPQJbOij+Try4+pVKzBJ598gvfee8/WS8Pv9yMjIwMPPPAAbr/9dpEcbU4ZMX/+fITDYfGUcxjd44A984xzgnV1dfTGa69Qx7otpPW9iN54ay6deOKJNHbsWDrhhBNa3EdzLSJb3KaxjIAESd0GMW524EiiQBHjQ4feRNTrHKLO3Q2u81K2UWAHUZ9ziZrKCAVzWZvIrv1JUuNEfYYzvrXPcCJwTnYk2y5czXhZkoz/n0gmbzuCiFKIguVEPYaY28XqCLs/IanvL4gGjyECiOIHiI7pT5TQiPQ4UcduJKWkGNdAIyKVDfQc8EsiqRNRimRpafkbokGj2XGbyomOGyraZbK2lt8Z7xOMP+Yc9NAJbLu6QktLToUImsGB+9h16X0OSR26UG1tLWVnZ1N2djY1NDTQkCFDbHxvSxwwEdFFF11Eubm59Ic//IHS09Nb9Vn+SHPlgD0A9uxnYzzJ1ql+C+l9L6Fly7+kvn37UiAQIFmW6fjjj6devXrRRRddRJ07m8BqAkbzSSA3g6YQBRiQ0L5NDFhCVYSy5QzoBo0mqtnAQEVKJarLJ0T2kdT3QqLGXWYCbNAocz0L+JjJqVFEkkTUVErU42QLGEtE4T1E3U4givpZxNWhK1FCJhzYRlLHriyRN/w+Bm4Dfslyc5RiSbJVsuQadHENKFhJ1P1EQtkKkk6+iqhTD6KEyhJyPYYwULa8J+p9DlHUT9R1MLtp9DqLsH0+SYPHOhJx5k0NCZ2osYQdO1RFFKoglC633YTKI8fRqlWrSNM06tevH93ymxuI/N+T2vvCNvX7zc3NpY4dOyZNjv6JzRWAO7it6Zln/4s2btw46lS/hVLKV1BqSge6/fbbiYhl0BsaGqihoYHq6+tp+fLl1KVLF7r+2qsI+zaSNOCXDLBIIjKUDy2ZCYw+QpdeJAV2sO0HjmCj34ffx8AJYBEspZCU2oHQ+1ySlDDRMX2JjunNjjfQR1JqR6ITxxFCewl7v2XrnDiWaN9GBtI9TyfSZUKsjqTIPvYaEQPuUCVR14GswfmBbUS9BxBRT5K6DibqfTZJ3U8m8ueY2/S7gKh2K/u7U3eiXmcT1awnGvBLBtZEhK4DSPLnkNT/EqKO3Q1w3sgav5/9f0T9fsEuRM23RP0vZoqIPsPZfvpfTFRfTNLptxId04uwJ9u8sfGG8QmdqHE3i+KbShmodx1AEqWw6xEoIuo9jE7t35keeOABSk1NZdvtySZUrCAC+6xbC8Dz58+nefPm0YgRI+j7779v/RfqpzCPA/b85+JCxVCxFroSBQDs2rUL99xzDzp27Ghbd/r06aLtYaIqC3pDKdR4GOnp6Qfne63KAs6L2vrqZkEP1bj24RV8qVFmbOOQuSJBU1mSq1nlgqFGsCbUgtUGv5qHRKzeVEGEa238rR45YHLPzkq6hhK2blOF/dxEVV6WZaSSpWewpji0warJ5xoVf/b3aWl1yROGweokTbHVCgsLocRjUPe2fgAo927duiEYDOKdd945nN8/LwnnuedcZnbppZdi4sSJyM3NBcB62D788MM49thjxXomWJvFCeFwGB9++CHy8phk6ptvvsGrr76K9PR0/PDDDyaARgNJ1V62RFWs3p4Yi9Wby3kDGyswWdc1uqHpTXtYpZo1CRcoNootisyEmqZCr803lRO8OCNYzeRl4VpDBaGZTXuUqJEojJrAr2tIxBssfSgUU00h+lLwqRqW9xqusU/tUBXL9cg0bhwKu2Zq3GylqWsoLCx0uQm595LQqzIhx0KYPHlym78Xc+fORSQSwXHHHXe4vnseAHvuuZtfeeWVyMrKAgAcOHAA06dPR8+ePQVYjx07Fo8//jjq6+tdf/i8Mu69997D9u3bXTSvRiUZB7dAMRJy2AJAvFGNJdoM7nVEtoYUS3QXyzIVDiJqNEYJ2SRqcXskzte3NHF3jhlKhGtY/wahL+atKmWLVCyKRLTOBG4uV5MNRUdTuQny/D1rCvTabUgYel9xgxJROD/+fvCxSma7TIsUzqWXRGsb8TTnF1xwAQDgoYceOlzfMw+APfe8JR85ciSWL18OgOl2X3rpJfTv318sP/744/HKK68gFrOU67q0oGy5/WQu69nLH8tLVzCQ4RSBoyEOX+4WBSY0Hm3KrBcE7w/Mo+/SFYw2sOiJ+TnbNcY6a+QjmrgrRh8H641ks6khdgK2rjHAtUa9um6nWOSwCcQ2ydoWtr2FshDv1TnvjheDuPSScHZTW7RoEa6++mqce+65rZ6snJOTg+3btx+u75cHwJ573hofPnw4PvroI2iaJgZrDhkyRCw/6aSTsHLlShOgLC0o3cDA3n6S9+ZVbOuZ0Z7iACpHf4amCmMdvixiAzRdNcBdjCyyR8Ds/JwNf6x9fw2db0OpJdrONHS5WWa/ByvHK4eM1yw0iXFtbMCva2zdquR+G67v1Rb1ZkE3In63a9waa2xsxM6dO5GdnY1PPvkEs2fPxl/+8hdMnToVt9xyCz788EMAwPvvv4+RI0eKz/snGmfvAbDnnrfFhw4dirlz5yIej0NVVSxcuBDDhg0DESvk0F2AxGoCVEVHsOQElm19TTGTdQbgJXcfiyER2d988QVvAVmcYUbC1d8mdUITx9T15CINNca2t/DN4n0IKsVKn6gmYAb3MD62fjesTdnNQpSY5YZhpTRkMZHDvH5WDtoOtFu3bsVzzz0Hn8+Ha665RlBIDQ0N+Ne//mWbs8evnRoPo6CgwFYpl/yZmU8YgUAAJSUl0OQItMq1bU7uOdwDYM89/zE+cOBA/OMf/0AoFAIALFu2DPfcc4+ospKjwaQfL2CJfC3JNLf1xOtVFvVEU4UJVEZ/XcH96hpsVXJKyFQ+2KJZGbbH/UCRER1beGB/Hj5c+C7OOe0EpKSk4JxT++HDWU+bib7afBYR87aUnBbg/HBTBWyj6znYhmsc550pRhrp1evtvC9XZ/BjcO7YuB56Q6krWHbt2hXDhw/HHXfcgfnz57tfY5cRR1bbv38/SkpKUFBQgMrKSlG67EYpuZUzt8E9APbc80PxXr164dlnn0UwGBQ/zk2bNuHvf/87VqxYkfRDb+4x2QrMvKRXgBgHNqEmkO2gqoTtCS4lbEm6WXr2VhnKCs7X8vFDnEM1ek18OP0anDKoF9a8NhHx1Q9hzWsTccrgPvjww4Vm3wfeY5j3lPBvFr0p9KxHHBI7oyk77wdhK6feYp6LS7N4k+ONWqiVttEM4hobwK/786ArbIy9EgvhpZdewlVXXYUzzjgDXbp0QceOHTF27FjMmDEDxcXF4jPT1RhWrlyJZ599FpFgQ4vlzK10rxTZM88O1Xw+H63L/Jo61uUlVca1tkQZmsyqxo47laiugOj4M4gaSkjqfwFBixuluXtYMcQvHraU6d7EWkV2OIYVN/QeZpQrn0ekhIm0KFGX44j8uawSjVLM6rWUDkR7v2HFEkNvIho0high0/BhZ9IbU39Jv7rgRHF+WVv30CNzt1LB4odJGnwZUd/zCfU/kNTzNCICUewA0bEDiKCZFX4wypIHjiCiVFbqfNyprJCi5+kkpaTar8H+LUTHn060P9eo2kslChQYZcwTWIvN7ieL8uWk62cUurDrcI643oqiUKqkk+T/nqQBvyRZ0eiRP/6ZevXqJcqRTznlFLr66qvpjjvuoBEjRlDnzp1JVVXasGEDFRUVkaZptHjxYtq4caP4zFsqZ26leZVwnnl2qDZu3DiS9ucQKlaQntDph1B/Ovfcc4mISOrQmbbW9aCP3nia9u7dS4sXL07aHprMynJ7DDErwxp2EfUxymD3bRTlwRIRKyPuNpAkShilwqxMWPTu7aDUFSUAACAASURBVHEKERKEeICk7iewMuFBo8yS3+h+kqATHdjBbhhEBkiCKN5ARWU1NHr4INs5jh4+iIpKlpF03gPshb3fkjTQRyQ3EekRVlIc3U90TD+zbwSlmMftPYyVREdqGJDu/ZYw4Jf23sC8Z/CgMax/ceceRH3OJ2loyHifHZLAF5ps7xtBIKgRkkgi9B5GtG8jdeQ9Nkr/TUQSdTlxLM2bN8/1s4QmE/Z9R3Lvi2j8jRNp7dq1rutt2rTpUIC3RUs5LHv1zLP/UcvOzia194Wkn3wDqb0upClTplCHDh3ojjvuoIKCArrgggvooYceol69etG4ceNIkyOsRFaT2Q4CO1hpsFG6S/vzGPBwsBk4kqS+57NS5d7nGICVQtR9CJGUwrYZ8EsWxfYeRtIxvYikVJK6n8gAredQopoNhKKFRP4ckk66kpUaFy1kywdfRhSsIiIQatbT2acOovXba2zvcf32Gjp76AlEalSAPe3bxJq8HzuI9X1o3E1EGhFJ7AFbSjWPu28T+7vrYFHmTP7vifQYQQmbTwB9z2eRc30hERIkpXYg6cRxJHU81tbEnYgIuka07ztC2XLz/Q8cyRrM9xkuyrKxbyOh93DCqROMp4DmDfs2EpV+Tin7N9Mll1xy6F+OH2MeB+y5523zlmRJ48ePx4YNGwAwLXESL6ypZqGBQ3Yl+EtdMyRkFmmbaKrOxtSzQokiW/JLL86w7JvrcTXL31FDecD55Sg+XPgeThncJ5kDXviORcFhJPh0NUlXLMqTVUfLSqF4sJQWW8uplXBSebbgzbnqwyqVC9eayUYx965IDBptSZbmlvTkUjy9KguxcOOhSsxa414SznPPj5Rfdtll2LhxoyswiMkVvAgjUtsMcDgapjsbtvNG6GKumylV02vzTfAzwE6v3WZJ6IWMxFwIHy58D+ecOZSpIM46DR9+sNAugTNKhBOBYrYvDoyiZDrXAHrZSM5Z+z4o7GYR3GNPuvHCD2upM1csuBSQCDmcpkAP1zi01c3LygAz6alVZiIeZ9dZN66bFtiN9PT0I/Gd8ADYc8+PtE+aNAmBQCAZFIxKuIQahd5Ubp9GzEE2UmsCdbDa0PSut0XCtkjRWqZsbdTO5VhK1KEtNqNbvWI1A0G36NNa/cZLg21KjZhZZOLfbKotLOOJ7DcTx4RkfvOI1Rv73OI4hyiL7vmoe2uRihxMkvlZLScnB/FIELolAi4sLEQsHPwplA1tcQ+APT+6fcyYMT9FxdFR45dffjlycnJcwFe1PKJbxvA4wUbQB5mmHE2sy0p89VCNHWh5pBith5XmsBVCVGWKMfZiu3gjk27V5luiasekDF03pW9VmezcrVVxvEcEB2kOqHz7aL2t6xsAc5qzAFvZuBGZHdASqmK7Idivo32+3MGMjzSKhRuRnp5+JL9rrgDsqSA8Oyrs6quvpi++XEF7Q8U07ek0uurKaw5b5vlwWkpKCj399NP0t7/9TbxmyqaMCRD7vjP65O4g6jOckNKBJK5sICLptJuJInuJSCIKVjDpWPeTWfZfDZOkNLHm5JRCUu9hTPZGZCSdQBSsZL2BOxzDVAKxAGvIHtlHUseuTLYGYskssR2xXsUDfUSsEzHRsf2Iar4TigMaPJbo2P7mRObuJzO1w8CRbIvew4jkoLF/nehAvtFHeJvRn3gkm5hhTTr2PoekzsdZehKznstSh45E3QcbVzCVsGeDbTkRsX0YDd0PZlVVVeT3++miAWGSypZTqqZTVVVVu3/HPAD27Kiwiy66iPZFd1F5ZAsljkWbGmq3t/l8Prriiivo2GOPpTvvvJNOGjyA6Vz7nMdAgjdO736SIaH63AC+S4nqCknqOZSo52mm1IyIAd3edQzcznuQqNsgom6DSDL0v6xZ+wjC/jySjulL6H8hSWICxbkkde5F1Kkr2+ex/YmO6UOSEmSAKXVg+tzOvZiGVyKivetNaVdKJ9bkPaEZ4A6m1W3cxbYfdBlJg0YzyVliMFFCY43ng5VEx/Rh58/fMxFJp97Izj/iZ5rnunwBwlKHzoTuJ7H3RiRuBtAUY5zSCUwRwUHeomzYtWsXLVmyhOrr6+m1114jIqLHHnuMHn/8cRo8eDBt3bqVnnzySVq9erX4nNZ+/SV1PBmk9rmQsrOnH4mvR8vmURCeHw3u8/kQl6OoCu1ANBb+r6EhfD4foqEGG8do7+Jl9Lq1DeLMNivCRDtImSWektQBmabCQY2Z3K8cMnhUfgxHWS9Pill7DFt5VDVm9PyNOSgQPk05YragVMJIBIrtNAcve7ZOMlbjxnnGGcXBKQlNNav1/Lm2a8NoAZUdy9JwXVTcaaYSBADmzp2LTp06JX0Os2fPFuuUlZXhd7/7HYzCsaTPq51oLo8D9vzo9kcffRQA68nLG6Mfbr/mmmswbdq0H/2DTEtLg1K+BklSM8GLbkmSQYlEkrWzl20su2O8vCqbTXeKM2Ar702aemxXBdgTcTzBF01OygWKGccbbzCBUteYooEn/vx5SGgyS9hZ+WHeZlPXzHLlqrXsONZmP7zbm63vsaHmMDq3ifPmndyMJN/WrVtRX1+Pqqoq3HDDDQJEhwwZIrqYcevcuXOLn9moUaPaA4S9UmTPjm7r2LEj1dbWUs+ePWnatGn04osvHpbj8NLSlJQUeu75v1JN+Afq2/lUevihR2jBggWt2rauro5OP/10uvDCC+nysaNIMib28pJYUZY8aBRRzXrLxN8xRHUFxjTk35B04lhWqFH6b5LOnkTU9zz2qK6GCZRgc9A6diNoMVZsIUms4IGXFA/0EfauJ2nwGFYlxh/ZDXOW7aJoIaug6/sLghYlSUohqt9J1PtccxBndD9hy0ySLnyM0ReCv5aM0mYfUSxA1LjbPsk4XkfUpa/l/d7EXk8oxkTjEUTUkWwTmqER+TcTDRhBVP+DZUhnnK0z4GKi+l1EnY8j6jaYpA5dKKHGSN/7HdsmlZURz5w5kwKBAL322ms0ceJEys/PpyFDhiT5GWecQf1696REzQaSj7+gTcM7D9FcS5G9CNjzFv1IP7K99957AFjv1l69eh2W9xMMN6G8MR+qpqIqVIisve+hKlQIWYknvU/r++d0g7Zvs/3R3TG/zWn2huxbjEY6e1172yarAniv4C32bmTWZujxJjuFYBRxmFGrwhQISjhJcSHGJ9kiaMWkDGyvy2Zns+pvjXM0zttos2l2cbOoL8K1rtcnoTl6CDcbuSviuPZl2dB1HW+//TYWLlyIjRs3NvsZqKqKoqIiFBYWQqtk+uJD7G7WVvcoCM/b5hysKhoLEIoEbU2qneu1BaRbWv/6668XP5pXXnnlkI7j5mlpaahs2o6sve/BHymDqisMfLUo/JFSmyjf5/MhGGpEeWMBgiEmW9IqLW0V+b/NtDo0qQfFIplytm40ZFdWfas/l9EF4Vr7/vx5rIDCOo+Ny9UsgKU3lrNHe2sLS01lUjNn4/RYvaWNpckh64Gd9paY/Licg24qT5J/JVRV0CisyGQzEo5WkuY1kWFvDO9oxG6dpmFQOqKhfCvkZ+FwGM888wxuvvlmDBs2TAxd5bP+jrAGGOQBsOdt9bS0NJQ3FhgR4g6EQiFkZGRgypQpGDp0KIjYFzocDQqQsn6h3QDT5/MhZESgzvWJCJ07d0ZTUxMAIBaL4YQTTrAdpypUiGj8xyfpfD4fFE1GVWgH4koM+fn50HQV/kgpFD1uA+C0tDRUGO+/MliId999lw3qrMlBQo0Z//KiCMdEDFujdDs4O1s3itfyZlqGcyaXKYukmnE8xgWvhZgFxwErah/wac6Uy0xeV9cY2EcPWIpDLC0jxfszolmeUOMaZlE4YjRr55G27diW2W48mcevT1IVn6XirqHEdl3tAJ4MviUlJbj//vvh9/tRXFzc7M26nRJxHgB73jbnEWBF43aouiIiC24VFRXYvHmz+RgfLMTXX3+Ns846Cz6fD7IaR2XTdgTDTeLLnpaWhqogW7+sId/1EZAnVTRNQ05OjvjB8Mi1KrQDBQUF6NGjR4vn/vTTTyf9yFJSUsT5/+lPf8KECROg6ioa4n6ouoq5c+di8uTJSEtLw+TJkxGNh1EV2gFVVxAIBPD444+LZXPnzrU8zlsSSBxYjF4DajziooQwAUqAqxJpGbitj+xukzCM/dtBPtORFDQUEsbkCpuSwjqjLlpnFmVYj2EdRRQNmCXFlmo8W/9gnlgU8/BURxP3TPGvbeCmQbk4jZ+PXpUFAFi7di0ef/xxvPDCCygpKbEBtVpxyFMsfkr3ANjztjsHvyeeeALRaBQ//PADrrnmGjz44IP49NNP0dTUZDzG7xAgDQBNTU02YJ4zZw6ICOPGjRPrx5WYK63xm9/8BgAEPRCKBDF58mQoWhyVwUJE4xGoqopdu3bh/PPPdz3naDyCqlBhUpS9ePFisd9gqBGPPPKIOGf+uqor8IdLEQw1Yt26dQCA5557Dlu3bgUAzJ8/H926dUNaWhrUCqaA4Hzi1KlTockmN5qfn49Vq1YZUSdrNs4na7iCiwCzbAePqzoe2S2z3KyqBqEosHDCvFGOc1y8ammqnrTMGJ9kbaCjWmfRGeoMHqlaAT/KSq8TDaVMtaHJrHGOEkmK7O2lxS03X//mm2+gxEJQyhl98MYbb6CqqgoAsH37dvz+97/HqFGj8O2334rJFkeY523JPQD2/NB85MiRqKurQyAQwJtvvgmfz4eUlBRMmjQJK1eutE0LZoBmRpYA8OWXX2LcuHEAgOJi1vvA+eNISUnBE088AQACwCuDhXjhhRcAACtXroTP58Oll16KPXv2IBaLYfLkybZ9zJw5U2xrjbLPPPNM237LG/Nts8PMhNwOVIeKsTe42wbWY8aMwYsvvghd11FSUoIpU6a48ok+nw8zZ87EihUrIMsydMfcNVfwFZOGzXJcwKkpNsuUxXLrVAwrj8uHd/KkW7jGmH5c6hhrr9gB2SKRszb9YdFwppg1B8BOLbh2QlMMUFWT5GfNmbUXRvIyM6mYkZGBfv36oXPnzrjrrruQn58PAKiursabb77JqKK9OZDj0aTvRzu5B8CeH7rfdtttApQi0ZArhztnzhwDgM2I0mnTpk3D4sWLoes6rrzyShARzj//fHz//fdiHVVXUNHE6I8lS5YAACZNmiT4uz59+uCrr74CACxcuBBdu3ZFamoqduzYIbblEfCll14qmuKouoqq0A5EYmFbrwZFj6MqVAhFjyMoB6CosiuQjx49GmVlZdA0De+++64r1cF90KBByMvLs0R/UTugWEFLCScn3hyTgUUSLFhtBzZbQjDTZUqxY7QRj2ADRUjoGvTIAQa81snD1kSYmOHm4HbDteb4oXijZVx8pv198ASi2N7O7Yr1mqFfWlpWX1+PgoICGz0WjUahyRGoFUc82dacewDs+aF7WlqaLVIEgL/+9a/o0qWLbb3p06cnrcfAzx2Uly5dClVV4ff7xWMlALz77ru2bmKqLqPCkvBLSUnB9OnToes6/H4/VqxYIdadNWuWkI8pqpx07E8//dQWtTsjdvN8dyAaj2Dy5Mnw+XxIT0/HggULsHz5cgBAXl4ezj777Gavmc/nQzwatlSHJY+rt419d3LK4rGf88Vx8EIH8bco5NhiRrwWfppNUrbOYFMNJUPURivoFatFd7GkXr3xJpPz5RG0teJPRMJuCcS4qcxwRPFJ6x2sr68Sgaok91EuKipCaWmpAGJ+/kcJDeEBsOeH7lzFUBl0j2xffvll9OjRw8LD2rlhN1C2Gv/xzJ07F9FoFLNmzcLTTz+dtL01IvX5fIjEwjZwraysFOf82WefIaoGbZpfAFCU5PN3M03XsC9cing8ClmNi0g5HA3iySefRG1tLWKxGB5++GHX8lciMiRsTH+qVqyFZhu5HmOSLSXC+vY6p/Ly0fKWMe8CBBtKTerAAu5iW5H0srd3ZJVvRtRqjZy5pljohbPt/LE1ardF5y4RawuN0JujGQ66vaBQsrFr1y7s3r1bLAuHw5g1axZuvvlmzJ8/H/FIU3vIzZpzD4A9/2mcJ+ZGjx4tKAA343Ky9evXi9fcEnZtMQa0OxCXTW5v/vz58EdKUdqUC3+kTKzbpUsXpKamGsdVW6RE3EzVFTTE/QK4G+J+298VTduRlpaG/v37iykYOTk5GDx4sOs1M8fYh6Ae2GGTWiWCe5FQZWhKzNZXAjBAyyrbaig1KIgo9Ppddu7Xn2enEaqyTHrCtm3cBG5H1GqWDhuRsGiwbhRC8InMfLy9kTB02kFleC308bWup1asxYwZM/Doo49CdyTrZNnluGocWuVaxMJNR7rlZEvuAbDnh8dnzJgBgAHt4sWLW/xRWa2tgAgA27Ztg6Zr8EeYSmHWrFnQNM11X1u3bkVhYeGPPnZVqBB5tSuNiHcHZCUOVVfE31yPPHHiRMhqXOyvqakJt99+e9J14jeuyZMnQ5XZI7le/W2zQJTUQ8IWxRocbrTOHsHaxrtzDa5V4eCIkKuyGO2ghE3QdhQ6JAG0hRsW+3FE4LYG7A7O12y67n5O5nrmcWVZRm5uLlasWIHs7Gzs27cPAHvSue+++3DvvfciIyMDOTk5rFjm6KEeuHsA/HPyn0ps3tr9/OEPf4Cu69iwYQN69eqFfv36Ye3atc2CGwe41nLEB9vuYNRG646tCnpB1RWRrFN1FR9//DEyMjKg6iqCcgCaruGrr77C/fffj3g8btvfgQMHAACLFi1Cz549k67V2LFjbXym6JjmjHwdEaTuLN3lfKpVgaBZGqRbmwO5FIwAYMfVZLt0rYUkmf28soyG6cnTKQRtYuWMRRc1p1bZjJDfeecdfPvtt5Z9rEciWH3Qz1OTI1DK1yAWbkQsfFRRD9w9AP65uM/nQ0yOoipYiJgcwZQpU5CSkmJb3hpQnTx5MmQ1JrS3Dz/8cIv9GX7zm98gFouhqKgIJ598Mo477jgALNnlZm50RGtAmW+n6HGRMHPuqzkg13QNYcWuXfZHSqHqCoJywPX4H3zwAQYMGGA/v6AZWW/atAkho2Rb1RVs374d06dPh6Io2LNnD37961+La3T55Zfj66+/hraPP9azajidc7+WaJg/3mtyFBkZGdi/f7+905gY66PaBnm6zqGrshdm2I9hjA8SCoa1ZttKZwRu44bjyd3NrNI3q75X8NaW6j9ra043/roZvjihxqBVZkKOBrF8+XJUVVXZdL/p6elH42QVD4B/Lp6WloYKS9UYANTV1WHRokV47rnnEJMjTEnQwjRY1p837gpIZWVlWLJkCZ588klcccUVtihv1KhRCAQCqKmpwS9+8QsBhhzkDhbdmlFny6DcEPcjr3ZlsxEvpw+CMpeeWYosIqVQdRVxNQJ/pFTsW9M1qLqcdPz169fju+++s+zHXD5jxgwQEZ566ikoKivgUHUFs2bNwoMPPoj9+/cDYHPIsrKyoOoK9oVLoekq9ECxvXLM2nvBAUbhcBhFRUVQ5agtkhV8bDO8qh00s1iUGiiCKGm2RbOKLQI2AdmIpF2OkdT7WPQNdvC+XN/rEl3rgmN2SeRVmQM1CwoKUFxcDL3KTjG0Y3+HtrgHwD8X5yXElU0McL7++mu8++67gjezAlp9fT3uuusuHHfccbZ9pKWlYV+4BLIWbRVPW1JSgoyMDPz5z3/G1KlTbcuqQoUobcq1FEAU4N///rdtnZbAuSV5WHPnxZJusrFP2QK05rlouib4XFWXjW3sxzrYTaOmpkb83x8pRXW4yBX0reuIa6rJ0KIB+7h1A/T0qix88cUXuOaaa3DyySfjtNNOw7Rp0wS9wc1WPefCq9p68Rr9fUXpr63qLm5qeUWXNgf369QBi+ZBW6CL8fCyqVV2iWrdjNMHCTWOkpISUXHI9seoGk2OYPr06bjsssuaLX45CqNeq3sA/HNyn8+H6dOno6CgAJFIBBdccAEkScKkSZMYPdEMeE2fPh2DBw82OqE1oiZUAkWV4ff7k384RjLMHTQ5+KhG1FlmA80333wT99xzj1i/JQ73xyTr7PsshGaJrP2RMihaDIHYXuN9sGY8mgG+xfUb0BD3u57XwW4UmsERq8Y+ne9J0zX7eamqiOjUirVQ5Bi0wG7Ewk248cYb8cc//tFWLMLHqnMzaQgjIrZGtMFqOwVQsdrB99rVC87kmtnVzOB1g9U2Tte6jCterKXLXC4GAKWlpdixw/7Z8oAAYMk0RVHw4osvgohVLT777LMoLy+3bVNfX49PPvlEVGK29++sDe4B8M/R+/Xrh4qKCuzZswcDBw4EkRktXH/99fjPf/6TBCTcNm/ejPXr1wu5V//+/ZGbm2uAjbWNY1nStm1JipnJLwaQa9assU05sO5L0zUWOVqm6zYHimY0K9uiZ13XEYjtFev7I6XIq11piZhNysRJiVjBU9f1JGqFd1VTddkA2+QbHT+OoseRlZUFORoSCaR4NMSy+IpZMRePx7F06VJMnDgRH3zwAQB3jaz5erY5gii4F3v27IHuIherr6/Hww8/DCLWI8MeLetIqHZJnLN9pG6JcIkIL774ouiNYa3a4zZ79uykCN5py5cvT/oOn3feeZgxYwZKS+3FG9u2bcPDDz+M3r17t/vvrBXuAfDP1YcPH45QKIScnBwcc8wxruuMHz8ekUjE9gW3AsvevXtx7bXXolOnTli3bp0jurSPGje3dY+ynYDJ+dqGuF8UR2RkZAhtLY9aGUdrVyrw7TlAN8T9IjnnBszWyNwawdsTcJymaDkaD8SqbdSKld5oiPsRUZqStq+vr0csHhU0x4wZM8QNMT093QJgyXwoYKkoq7JXqSWvZ45yV2Ihy+uy8VoYV1xxhfj877rrLoc8LJNRIxYFwqZNm2zHsM5hu/HGG00u1qATAIgScjerrq7Gtm3bkl5viUq45JJL8Oqrr6K62jwvWZbxySef4Nprr0Vqamq7/96acQ+A/5e8rZzXDTfcAF3XkZGR0eJ6F110ETIyMgC0HMUqarxZgAXceVtu7o/1OxygqTYDljKCckAk4Kw8rqLHjdcZv+qMnONqxKHtNRux67ruUEaUuZ67/T2a6/H3GojthWZwyc7tecLO5/NhxowZglpYvnw5+vXrZwOwWLgR9957L0aNGoVx48bhyiuvxLvvvsvUE43lBkjatbhWy8nJcZV5uXUJ69SpE/Lz81FXVwdZlsV2ukMx4Wb//Oc/xf/599H62ooVK6Abs+di4UbRhMnKL/PvG3u9dW0kJUnC6NGj8dZbb4lEJ8BA/YUXXsBpp53W7r9Rh3sA/L/iY8eORbwpjIJXP0bj/rpWg/Djjz8OAPjLX/5y0HUnTpwophS3hXvloKnocVQ05buoD5If663GOVjG2RbaqAFOd1ij3KAcQHH9BsG78v2GlSZbcx2Tm5URVYO2pBzjgMsQV8Miyub7U/T4QRUc1mXm/82kHl+WmZmJzp07i5vnlClTsHz5csTjcdTW1mLChAkt3linTJliAy6taY+rXjcWi2Hjxo0iQcZB9LHHHnNNYL300ksAIJJfCTUO3YicP//881Z97tu3bwcAfPjhh0L9AgAFBQViaKlStgZPPfUUCgoKhHRNr8rEqlWrMGnSJJukTSlvfRFFamoqfv3rX2PBggWor68Xx163bh0mTZqErl27Hg1JOg+A/1d8+fLlKJz5Cd6RLse2f2S0qdrnnXfeAQDcdtttB13X5/Ph73//e6t+gNysdIKqKa6P9bIWc6UtrFFuIFYNWYvaSn85WObVrjQiT5NLVbU4ArFqwcty4OaRqT9SiuL6DY4bQpkB5IpYhwN/aVOuOLadZrDrk52lytZzbYj7EVWDLeiaVTTF6xCJhUVkuGDBAnTv3t3185g2bZqo8lIr1iIvLw+AnQ9OqDK0SqtyIS6iT96YyApEo0ePhq7r0HUdADBnzhxcd911iEaj2Lx5s2jhCUC0CXWz66+/HmlpaQCAYDAoXv/uu++SQH/y5MkGv8zOkzfAV2tykJCD0KsyIbt02muNd+zYEePHj8cHH3wgziMSiUCNh0Uf4XYCYQ+A/xe8U6dO2L9/P+RgBIUzP0XwQNu+UJzDjUajuOSSSw66vs/nQ1xxl6IVFRXhueees3GBdo6WS8HMggfR6lG3J4/sSb1SA4w578upAVlEuUlqAssjvxl1mpEspwms20TVoKAywkoTqkPFiBlRMEvS8Qi4DM4ImN8sqsNFlvcoi3M1eWh3XbOVBlH0OObOnYvnn38emqahrKwMo0ePdv0sOJjFwo0i2rNSDaJZTQOjVZyaWev+unXrZktsZWRkiIKd+++/HwDwzDPPmJ+RatIRzz77bBIIX3jhhZgwYYLttW+++cY1+pw8eTJWrVolErz8vf2UPXy7dOmCiRMnoqCg4Gho0O4B8P+CT548GQCLRmKxGL744os276NPnz4oLS1FTU2Na+MYq7u1n3SCphOYrSDHdbE8WlR1BdXhIqGccEaRzqSeNTKtCe9GUA5A13XIatQWAZvVbezxvyle6yoDc9IFPEK2bmvlcGNqCJquoSleK0DX+XdzXLEVsJzXzfme+Sy6kSNHoqSkBLquY8aMGWKYJHfeDlOJs7lruhpHPBo0ot44VDlmS761VJY7f/58cU5fffWV7ViSJGHTpk3QNE3wslZlxh//+EcR8Vpt/fr1SbRFa7+Xh4smOEoKNTwA/m/31NRUlJaWYtOmTSAizJs3D+FwuNlH1pZ82LBhCIfDqK6uxrhx41zXOeWUU1BTU2NLTn355ZcuQOsGzPYyXw5Y1oSVpmuC87VGyoFYdZLCwcnDsnXMffEEGz8fDvpmcs+UmHEAVbSYAFSnkkLVZYSVJmTtfc9GbbC/95pct2UfVmtJI229TpXBQkRjYVvXrq5du2LevHkAGC97zjnn2D4XNgrJGK1evgZLly6FpsrQ/XlQR51JCgAAIABJREFU5LitGU1zZbnjx48X5/Hdd9+ha9euSZ//L37xCxZFV5q8rBVou3TpYuvd3Jw999xzzbbpPFLuccCeH7LfddddACAGQ957770AgHvvvbfN+3JSC9bZbMcffzz+9a9/Jf2QSkpKIEkSLr74YgQCBsAFzcY19uhQBS9MsEaJ1uo0DtCaAcZRNSikXdYIORkkC9EYr7U9yjsBOqqGBa8ZURod+l0NugH+1aFie3WarqC4foOIxJvitUmJRCdg8xJmE1iNG4Mmi6q41atX2/azY8cOUbygaorrlOjrr78efr8fsVgMjz32mAAxZ0TH5GtGr+G9OUxX3EK016dPH1EEsX37dhx//PHNfk8yMjKQUONQytcgGmoQ71HXdbzwwgt47rnnALAJFNysDfS5ZWZm4tlnn/1vK574Kd0D4J/Sj/QdVZIk7Ny5E7t27UJTbQD5hgKirKwMGzZsaPP+2JTh5AjW2d93yZIl6NatG373u98BYHpPIsKpp56KqqoqKIqCgoICG4hxQHSTfZlSsR2IqxEoWsxCCdhLga3lwCbAmvwqk5aFRWTK5WRRpcmFi1YsUrFqh1rB3H8gthdBOQBZiyIQ22sUbfCbQikCsWpXhYW1GIWXPrvpnd2eGPjrblOi+/Tpg2XLlgFgE4AnTJggWlry758TkK3L3D573hyprKxMFOc05927d0dtbS2qq6sxZcoUAEB+fr6gJWLhJuhKFLqR6HOzaDRqKCsyj+ZeDYfbPQD+qdzn8yESaETBq0sQqmvA1VdffdiPefPNNwMA9u7dayog/pmBlStXAgDOOuss2/qjRo1q8UfI+0WUN+Y3+5j84IMPivVTU1Oxe/du5OTkiNf69u2L77//nvXj1VRLU3Sj2YwRGdr5zh2CC7YWP3Cws/K6MTVsAygmFYtYKItCAcb+SKmgA3RdR1hpEg177Ek0GdXhItv5RI3Em6mKUF0jXy5vs94oFC0uomKrBK82WtGC3tm83hs3bhSl4Yom45ZbbnH9vO6++26Ew2E4RzJZP8/WBAT8KWrfvn0YOnRoq757t956q7gBAGzUE4+4tcBuMelDKVuDRYsWAbArM95++22TMjm6evQeSfcA+KfytLQ0bH9tCd6RLkfhTBZNFBQUYNasWZg4cSL69Onzkx/TanJTGIUzP0VTbQDXXXcdVFXFyy+/LNb1+XyINYZQ8OqSFnXCPp8Pc+fOFcMnW+Irx48fjyeffBIAcNVVV4ntn332WTHxwr2IQklq/yirMSEL0xwRqTVC5ryqM9p0k3vF1QijPTQFihazJe+clEFEabRF07xrmrXDmr2Qw5TQxdWIiKKtoM3la/wYcTVsicCTtbp8+Ghubi7mzJmDzz77DPX19QgEAoKTT0lJwcUXX4ynn34a69atg6aZ78MtWj6YT5gwAbFYDMFgEOedd16btrWWrD/yyCMWNUYTYuFGG+XxxRdf2JQZy5YtOxqSYO3tHgD/VO7z+dC4vw7bX/sESjCKwsJCfPXVV7aprNu3b8dbb72FW265BX379v3Rx+rZs6et7PL6668X2lxOByxbtgz79u1Dhw4dQGS/QeT/8+MWf6hvvfVWs4/GLdm+ffugaHbgdtfDssiOKRdiCMSqoWjWyRKFzSbBeNUb43SDUHUFtZEKxNWwSNoJHa/m5IFlQW+Y1XIMxENKwEisWaJWLe6Qk9n7U/AomkvpWGRdirgaRnWo2LiJ2FtZarpqgL2K5m5wnEcPhhsxbdo027LGRvORPjc3F++//z7C0RDKGpL5Yv69bC4K9vl8kKNBQRW0FQRvueUW0RvCSXM4j+vz+RCPNLE+EkoEsXDTQWmRn4F7APxjvLkvNY/+tm3bBkVRcOONN6JDhw4YMWIEZs+ejR9++MHWW2HHjh2YPXs2fvvb36Jfv36tOvYdd9xh60LGW0befffdAIATTjgBRKzMGABuuOEGcW7hugYUzvwU8cZkQbvP58P8+fOxdetWaJoGRWOPtaFIEMFgEEVFRRg6dCjOOussjBw5stl6/uYfsWXH/00A4pFidajYRi1YI2T+mB9WmlAfq7Ftz6NlVVcRUZqg6ZqgMqrDRUJVYa124/vn1IeqsXMKy/XQdV0kChUtLvZpLShhdIpuK4FOlq8pgrM2r4WpRW6ud3HyNbA3nq8MFiIYbjpo28Xx48ezsUiO9bmnpaXZKtLaGj3blBet2D49PR1a/W7oeTN/zrSD1T0Abqv7fD4ED9SLhJfb3bt79+7YuHEjZFnGY489hvT0dEQCjdj2zww07q/DPffcgz//+c9YuXKlrUJo586dSE9Px2233YYBAwbY9nnqqacmdSm7//77xXInAHfo0AH79u3DsmXLxDrXXnutUAFY55NZ1Q+c7ywuLsZXX32FF154Aa+//rqIuDp37mzbzpx7Zp9CYZ1MwR/jWU8EzRYxcgWCM6lmT5LJguO1Kia4dljVFMHx8knHvOqNJ79YIUW17TiaRdNrL4k2bwwcBO0AqiCqNJkgbXmvzU3QAMwOa9bI216qrECWzRuV8ynACs7ljQXNAtidd96ZBOZu9MShamHbuv1Ror09mtwD4LZ6Wloatr/KHuULXluC6dOnu6533HHHoaioCEowCv/6gmbLhFNTU3HxxRfj8ccfxxdffCFkSACrKnv77bdRUFAAgPVXnTp1KrKzs1FdXW0DQycAE7F6flVV0b9/f/Ean+JQWVmJY445Bj6fD6tWrUJl0M6ftmR1dXUoLCxESUlJkqLB2j/XbELuVqlm71Imq3GbJtjJn0bVYBK4mRVt9oiT/82B1a7EKENciyapEni1nbVMmM15Y1E3B9DSplwbP21tvckldk7awXp+zg5ruq47eOKIS9Tsvq8vv/xSgNh1112HXbt2JX1W0XjYlZ6wDgM9FBqgrcqfo0B7ezS5B8BtdeujvBKM4ocffsAZZ5yRtF5KSgrWrVuHwpmfYMXIhxCvD6LgtZYTYEQMkC+66CL86U9/sjURAYBQKCQm+r7yyiu27TgAn3jiieI1XrP/pz/9Sbxm5RSXLl2KcF0Ddn202q4gaGH4Ije/34/du3dD0ZiiwQSnvQYQyraEk71tpEkrsCi2FLqu2QDa7Otg5U/tvC2nF5z6WxbxmoUe9oo61dbbwU4raEairgyarqKiKT9Jtsb34Val52yJ6VYMwqkOq6zOWZrtlqAT1z1SiupQsfl5aXISMNfW1uLMM88UFXLWgo7u3bvjoYceQjQeQWXTdlfe2PMj5h4A/xi/6qqroGkatm/fjgMHDiAUCuHOO+8Uy88++2xs3LiR/SiDURTO/BRqOI7ly5e36st+/PHHi5LQqqoqPPPMM3jssceSyjl37dqFzz//HBkZGWJ9KwATEdavX48dO3aIv8877zzbPnhkvuvD1di4caP4sfbo0QP33XcfNm/enAQCTl6ytDRZ3+rWY8EKdvXxGts2Jo/LwUiFrNob9DAZmAlevO0jV03wf61gbM50YzeCuBoRcjJrdzN+HmYLSvvYIn+kTBRrxBxqBn6O1vfHrpPsoFUUh95YEb0j7N3YnFWEZh8LK7/tRk2UNeTjpZdewj/+8Q/ICks4RuNhLFu2DPn5+YKCOhTlhOc/mXsA/GM9MzMThYWFGDx4MNatWwcAeP/99/HCCy8gHo/bstUCQJqaDtrs5s4778T+/fuFjOzYY48Vyy688EIAwMcff4xHH30UOTk5BsAz5QXAlBaffvopJkyYACISI36uvPJKTJ06VUTQ3JRgFAWvfoxQoNEWKVl9+PDheP3118V7cgOK5CowxRLtygIoOafq1uzcHylDSK4XEjVn4QLvravrOhRVNmRdsgAnDqhcJcGTaaouJykP+P+dtAi/UfC/dV1HTA05gNoEQqa0MCkIJ+cciFUbiT3NtVm72I/t6YBFtVa+vLh+g+1GYW3os3//fkRiIVQ2bRc3QOdnwm3//v3IyMhAOBJsVjnh+RFzD4B/rD/88MMAgKFDhyI1NRUrVqwQX/KCggLRlITP63r//fdRUlKC+vp6XHDBBUn7O+2007B69WoATIhv1WRy3iwzMxMA8Mwzz+Drr7+GqqoiguXaYzUch399AdRwHNXV1di5c6cNcDdv3oxIJAJN0zBz5kz2Q62qQiTQiPx/Np9Y9Pl8CEdDlsitzPZjt0Z5TfFahOV6yGoMTXKd0STHjCormvItfRnsEbAVFJPBiml6edUZj7KdiS17Ex7FiIRVzHr7ZQw5czBSUlJw5tln4O9z/iwe+a3nxqv2OCVhjYatx7L29+WAyc+bR9nJ/Sr2wnlTsZ4n54CdHLPzxtAas34mq1atwtVXXy2mQ3hc7FHhHgD/WD/55JMBAE899RReeOEFW5crWWYRTE5OjlA5vPPOO7jppptQU1ODcDgsKItOnTrh6aefRiwWQ2NjIx588EHR/o+IMGbMGKPIwoxyAUBRFHz00UcIHahHwasfi2Xx+iC2//NjxOtNdYXV3n33XTFt4Le//a14/WC9hFmZ8vYWAcBUGjAAkVVW/OB8vGbJLc22jTUZZepry5KAiPeHsAKvkwfmPSGsx1v44UIMPnkAXlvyJFZXLMBrS57E4JMH4IMPP7DQGrIAVcXCifOBnM0VfPDyZw661vfjvIlw46oN55NDclRucsxuVXPJgGvSQBs3bkRGRgZ+9atftfvvxXNX9wD4UNxqb7/9Nnr27In+/fuL1xoaWKMSJRhF/j8zEDxQj1goDP/6AijhGF588UUxFTYjI0NIzwYNGoTJkyfjs88+QzweF+C4/dUl0OLmjy8SiWDnzp02ntYaETc2NuLNN98UFVYAUFtb6/rjVYJRbJ/5SYsRcDDUiLKGfIQjQcyYMSOptSJ/5I6qYUEzWB+bOaC6Pf47q8hUXYWsxRCW6y3NexhHbOdRVQvP6+RZmRxO0eI4/exT8dqSJ5G19z3hry15EmecfZpNEhdVg0nKBM1KXWh2PbNmlCjzyNU5vt7JAVuB0l7ooSRplJ3buHVYsxo/54qm7fj73//e7r8Pzw/qHgD/GD/22GORkZEh+NdYY0j0fuDtIQGIhAcHxZJFayA3hpMi1PHjx8Pn8+H555/Hli1bxOsVFRXIzMxkx3ntE0T312PXR6vx5ptvYtKkSXj11VcFbWEFUq7QWLNmDWJNIcTrgyic+QniTWFMmTIFscZQUkTN7bvvvsM999yDU089FUT2R1W3x1ZnRy/ed8EfKUWjvN/1sdkZ6Tob4bDOYzsM/tYEHmtU6dZjIq5GEIhVozpUDFmNWcqIC5GSkoLVFQtsALy6YgFSUiRYe1GYQK8kAaN1vf/P3reHR1Wd6yco1VrrqdbH1hs/7bHUqrVqzzkNamvFHu/U46U+WlvUNmqtWFsvGKResAelmkBbIYgWL8glJCAgQsAkEBIgNVFCZpKYkElCyG0yZO77vvac9/fH2mvttfbMBLy0Vpn1POvRzHXPMPPub97vfd+PNuuI9LrZa2HSNVatM8rF+/rZSYhOn3Cbhd5gevk+2ekHVc8sOcvtf9mdA+APuydPnswBVqw2bdvGO++8g/r6egDUEjx16lQJFA1FS+NsxUUIwbZt2zB9+nScffbZKCgoQHxkFN1lNbANEyON7dCTKgoLC3HMMcdg2rRpaRyvd4VbAvAXr5SeUzwGJnVjJxMz4Tr1wuEwtGgiq+mkoKAAhqlLgMKaTpmMFaoVE3hVWV7m1QeTlJlWiTLeOBM1YRDVqU5NTxQlfZ4zz5qYsQI+48zTHUC1pZlwLOQdEKtPwmMpUx6agJ0MXC7a5DI4r/LBff2MQ5YdcpnC3A9EPzz11FO4+OKLc7zuZ2vnAPhg9zHHHMOlXp2dnbjnnnsQGxnl1WZ9fT0CgQD/QoiVrLishIbusi0SlQDQPN+vfOUr/PmYQSKwvNoFecNC67wKaNEEtzR3dHSAaCaIomO4jlIb8+bN44BKFB1mNMmPE2CNOj+I4lZaDJS7y7bw+7LnYylrXm64qKgIQ0pAys31ZuMyp5qcVGZxIFatWBpAsbhHVoky8GMDPWkGBFVVMMBilSebOdcR2QGDqLxSXbL0dZw04WsSB3zShK9h3t/+F0G1Oy2Mh8rg6PtFuV0xs5halmV6JT2fODOnSytYVp17OWUG+jLweqdAy58d27b5Z4GFIuX2Z2LnAPhg9rXXXouBgQHYto05c+bgyCOPRF4eBUnmUhsYGMBzzz2XJgvLtESlAltdXV046aST+OMm9kfgKymHlVA5KHaX1XAwbmxsxAUXXIDm5mYoQ6NS8y0UCiG43Yf1k6ZlrrQVI72p58yTI5ohgbEZoylrymgU99xzj+SeKiwsREKJYSgZkKZDyBymiYg+lNa4SphhJMwwkgLHy6RoUSMoVLaygkIGQjf4x7JliZgI6Cwe8g8Lfo0zzjydqiDOmoglS1+XKlHFjMCyzbSqNdusOa6UICbXD8thPW4msTsM1PKAqWzE8Fa/7Hm9jUbpNvv2SYHntbW1+NOf/pSrhP/1dw6Ax9pf/epXsXTpUgDA0NAQ7rjjjrTb/PjHP5a+DGNRDGyJYBnv6peA8JFHHsGf/vQnN9pybgVSdkoATlrJ3nrrrXjjjTcAADaxZTrEqVyNSEICeXZyMCIJDs62YcJfUg6i6Ijt6Uf9XcUuZxxJoP6uYgS30/HitmGi/51GkJSJvngrTGJA13UX2IWfybLbyzVY0OrS5CDJmm9i6lhHZAfnkhkIiuD9fmgDDKKkBeEwVYTXqODyuz3Cz3xXJ8yqypTDx7KTgVvFu1I1ZlmOGkEpytIb1sPCg9z3IPOJg75vYo6wG+LuvqeiEy+zLTnTfTIF8OT2v9TOAXC2ffPNN2NkZASWZVGFQBYb8VFHHSV9+K2EhoGq95Aicrf6nHPOyQjSEX9PRrBmjTcroWaUoXmXGVfgn1sOM65IlbK4xOcNbvfDSmgYbQ5g/aRpVGFhEe7asyyLAz5RDJgJFf6SclhJLa0SMwwDZWVl+OMf/8ify1u5sXxdyzachhPxaGXFUUWucUNUNLiX2x5Ac4dpsgQz0dTAGnEiFcIUDOzYZOu03Bh0p20wI4k7iZmdUMSwdiZdY8/ltS4nzLA0YslLQTDt+B/+8AeUlpZm/Tf3/juwDGZ2eW/MhxkzZnzq36XczrpzACzugoICzJ49G7W1tQBoQPa8efPgK1nJw3cqKytx9dVX4/bbb0d5eXlGx5vFwEpoaB133HEuWEaT8JeUw4wmoe+PSVWquFLExmiz3ETLVN3ahoWBqiYQzQDRDEonOOBt2zYf5igqJACAaKbwWHrGY2CL0xIrakBSFvbG/dANFTU1NWhvb+c8JFti8y39/+VsBdPWpKrV22Rj7jcvyIkh7awKFUGVaXRFo4MYsG7aOtcRp2VK2G5Sms1PFsSlEWwiTNdwxx6xnAbx8by5Ft4sYLGyFYHfsHT6K4AQPP744/i3f/s3/PCHP8RDDz2E6upqJ3tZbszRganu5X19fXjwwQdx3HHHferfr9xO2zkAZrugoMDhQWmlGQqFcPPNN2P27NnQInEHuFR0l9WMWYkC4Glp/pLMmbluE0w/4OMRxXAogVUSD+wvKef0hb+kHNpIBN0ramBbBMQkUAZHQYz0UJcUsfnrZFREd9kWfr3LYbPXqiLS1iuB+uuvv46ioiIUFRXhySefREFBAY444gh8//vfz/ga7JTNZVmZOc1WiYbIxLkaRIEt0RFtAqcqz3IT6QzFikshQZKDzVFNsGMT7yc2vFgT0FtxZne6pUdzMjNHKkNDji0v9cGut20bDzzwAE4++eS0z+wf//jHtND2TEvXdSxZsgQXXnhhzgX3r7NzAJyXR11te/bsycjfMtBLETvrT3txEc3MWAFnWgNVTVg/aRqC2/2Id/XDNgmIZnIONhMwc0lbJAHbdFUKzKQh319FYHk1Pw4roWG4zie9znBLAETRYUQSUmymv6QcTTNeRveKGpgxhT5OktIr3d3dIISAKAaG63zQEgra29ulKE3+fng0wq5iwq2ArZQBzeF0B5IdDn8qT6JgvLBsgrAlN5lId7jpZpmydd2oSpEqICnLyX2Q+d4B5QOBomiTgNcrR8v4mRBkZ2F9QFKNeN8rrwnDq3qor6/H/fffn3Fw5quvvspv5/f78ac//Qlbt26VMqcBwLIN9GaYIZfb//R9aANwfn4+7rvvPiSTSSiKAi2akH6iA5nkWavGrFhb51Wg/q5iJHqHEevsHwNERYmYU3GuqIGV1DDa3JVVwQAAid5h1N9VnPGYgtt9nLJgIOovKefqBhaN6b1PYFk12kvX8dlyTH1hmxa6y2rQNONlBJZXg2gGbJMg3BKAEUmgf3OjBLJsogRbLD7RDdjpkdxcVHEw6MldoE0rnSR5I45FRJIUcYwLdBqFG1LThkyKAqrnlUfJu7Zmr7W4zaM9buUgL2YcG0SVpjh7m2HZmmPsOvE9ECkZ9/0YkFQlmcwXqVQK27Ztw3333SeF999yyy38Nrt27cK4ceMwbtw4fPvb38btt9+OnTt38uznXBLap74PTQAuKChAcXExmpub+YdV7Oaz9dBDDyE2Mordz9NJFmwA5VhLrFCzgSgDwkTvsOSSG2lo44BsxhQQLZ1CEJUMmbhjohiC7tetgEf+3salZpaiwTaFIJ0MVbttEcS7+kEM57q59DpiUEBuL11HKYqkhn2J9MoyHYxMCXilYxZuw6ZcMCME42DFKlWcbMHyfUPaXvq8Nm3GRfShtMhI70SMIaXLE6BuIm7ulwA0XcEgN+YsYnALdlgfBJBpLJMrWfMuVrF7w4XixigMU6fONiWOhx56CM899xwaGhpgWTKwp1IpbN26Fffeey9OOOEEngPN1jHHHCN99pmlPFcBf+r70APggoICKOGY9BNfVVW89dZbuOeee3DqqafikksugWVZeOedd9LcRV1dXWlgaCU0mEnKq+rxJPbu3StJxtIARzdhxhUMVDUJOl8Vw/UCPTC3Ik1JAWSWuXm1x1xn7AD4QFUT6u8qBjFMhFsCsJJaGrhnk88RzeCctpi4Rs0kJrVXG5r0k51NC5YTvNLBmS2vBtgL3l4VQVgf8NAZJgdLdj+vFI1ZfcWoyEwNO9PWeSoZa9pl463diR9yToOYzEYlbyaIbWSsipl6I5MaQ9OVjBGhRx11FC699FI8+eSTqK6uluYM2raNpqYmaIYqPd8555wjfQdyHPC/xD70APixxx7jYOObW45XXnmFGyvEffvttwMAFi1ahLy8PEycOJHHQaaDVgWC231pADY0NATv4pbfaBJGOI7A8mqknFhJlx5I528BWpUyDbGVTKdJWuetQorYLmDOreC8sBlT4C8phxGOI9wSyHJc9IRBNArUKWJTqiEc59Wx2DQMNbbDCCcw8E4TBVfFbVp5pVjsZz8bvClWg9l+smdTETDKwqsPZtRHUO1GSOv1VLJuxRw3QtJoIZfecKtbyzZ4Q00Mife65QA4lmMxMMhNVxN1ymNlOWTiqg+WIhg/fjy+//3v4+GHH8a6deugaa5UsC/h8tJ33nnnp/79y21pH3oAzMbHM1phrCqAjXpPJpMwDAORCB29/fDDD9MvjVDlUkmXXPFmcsWJYDlc70tznBHFQIrYkgWZrXhXP8y4AjUYligDyu+ucjTITdBGIg7YJhBq/IA/rwzwcmXOeF3bIjCjSa6AYCcL2yQYqGqSjolpnm3DpBSHYWLHjh10PD1xZ8SJwyupFlhHUO3mzy1Kw1zZmGw8YEoFxsmmg6HJOWIWcs4qa8bhUslYjwCQ7kigTDGTqVTKybWQU84yg6f7C4DNkPOqMxi4H2jx12WbmDZt2of+jE+aNAlJJc5phueff54/9qpVqz7172Bu833oAXBe3sH/BJs8eTL/4GqahhNOOIEG5ITCHMRsk2SVkmWnC1bBjCZBFJ3zuXte38wT0pT+EZ6aZsYVSRpGNAMp28ZwnQ/1dxVjuM4HoujcrWbGFCpHM0yEW3u4w802LYRbArJqwiQcfLtX1MCIJKEFI2idV8Gbd17AzUSrdJfVUNB3eGIzriDcEuCNK9eGK+cZsNAel1M1kTDDvOplK6wPIKTtlW4bN2isJgNbWrEO8IqbZU7IgGtxKoJVqAZRJB5WrG5FmiLTmPj0mW+uTZkCqQz0mUB8rIYdW9u2bZMC+j/KZ/z888+XHnP8+PGf+vcwtz9FAP5X5qGOP/54vPbaawLoULAtKyvD888/z6vJ4HY/9P0x7H52GRK9w87txco0vSoGwHMaKP1ggxiWfD+TUFCt90lSs9Z5q2CbRHDGuVpdxukSwwLRDIQaP6CjewTNL9EM6phzqAiiGrwi1vfHMFD1HldmMF6aNRSJYoAYFhK9w7AUTWoA2oYl0R6jzV1oL13nVMGOlMwmKCwsxCOPPALbtqGl6XqZFph4gE6mBpirzaukYBOMva4yQOZkiU2kv2X9L5Hsyaat8ekb6Vx0G7+vKE8TVza9r/c2zCXoNWh418qVKzFhwoSP/Lk+5phjpMcTJ2jn9qeyPx0AZlVkS8lKxENhXHTRRZ/2G8H3HXfcgdHRUZimiZqaGjeTQahiXXMElZLZpsW5XBGIxNszoOQ/7edWwIwrACDxtsy8wZQGzGzBgFyMl2QyM+aQo7GV9MvrzX3wF690Mx1MAmKYGK6j8jOm96VWZHpyie3pB3GqZqbmIJrBMyxEHplxzEx5QRTdqbAtl15ItOKpp55CYWEhbzzJjbTM4OWVibFR9kyqBSCtKhU1tN4kMtPW00DUq1DIdFJgCWxihW0QxeGb98IgShpgerMbMi1x1pzo7htrrVy5EosXL846w+9A+4MPPuCPdeWVV37q37lDeH86AFxUVCQBm23b2LNnDzZu3Ii//vWv+O1vf4urr74aEydO/Kf9VDrzzDO5Bbmurg5nnXUWz+OlQCtzd61zBU7VCUvnUq9Ntc92AAAgAElEQVS5YnVKQbO9dB38JeVYP2karISKYL2P23+ZGkKUgckgT3nhUGO7J17SVVDEe4Y49yvGSLLcByOS4IAPUIC3FE2uvJ1K3K1+K/jfRDEkMLYt96c00z4Tjd6G6YYHqt4DEeyy/f392LFjh/ST36sLZkueu+Y+hkEU/pNeNHiw0BwGyiyox5vM5jYG2zjlkX3gpehoc6VnMn8sX/dRViZ3n/i87JjcOXXucypq8iOB8KxZs/jzFxcXf9pAdKjuT7ECHhlFS8lKaJG4lKPrXbZto6enB5s3b8aCBQvw+9//HlOmTMG3v/1tHHHEER/7TTjiiCMwa9YsmKaJcDiMX/7yl8jPz5eOdfXq1RIVwYBKcpXNrUBKGNEjuuaC230wYwps00K8qz+NGyYKzXAwIgkQISdYatjV+aCNRDBQ9R4ASmOwpLLR5gCshIpwa09aU89KqLCJY03WBNrAJCC6KZ0Iw7sD/ATAQJTZlMWKmla5cspacLufA7EI6iN/bwPRDGzduhX9/f005Ocg+FDxNm6l61afjCtm1IDXEsyafuJziEYP09b542WyGbPjyWSZptWwlVGJkem1HGhlq5TF5mTCDMO0tbTjCWtDH9lM8aMf/Yg/VygU+pelBD/H+1+HAz7++ONx4403Yv78+XxO2oFWKpVCX18fampqsGjRIjzyyCO4/vrr8Z3vfEca557t+V9++WX09fUBAFasWIFvfetbmDBhAs4++2xMmjQJl19+OW688UbU1tam2Xfr7yqGvj8m0AOOk80BMCOSoFyrYMgIbvfBSjhVp2cskRl3RxUx/S+TotmGiaGtzRh4p0m6joFc/V3F8JeUI2XbiHUNwFI0DG1thqXojlQszqtyel8Nwe0+DFQ1wQi7srfdzy4TGm5yRS6+/qGtzRkNJiIQs5MSpUgqUFpaiiuvvFL6GX8wnKmrBZapBPq3O4XZ62BjmQoGcWV8zJXnBdZMc9vY8YjNOTdnwjt9uS0N7Mfif7NVtplu4yax2WnH83EqYLZPOOEE/nx9cX/OnPHP3f+6Koivfe1ruPnmm7Fw4UJ0dHTwD2YqlcLQ0BDeffddrFq1Cm+88QZ27NiBkZGRtA/6wMAAamtr8be//Q1FRUW46aabcN555+Gyyy5LM2OMtcRAHKZe8Gp0AyuqpVwHoug8Z6G7bAuIZnCgZIoEYhHYhonushoJqJP7grANC+HWHphx1QFulVfJrfMof0zdaFvoJIyECqIaaRZn2zDTeGyxUh35exs3VDAagt1GvA+jPYxIIs1J513SSalsC9RwnJ9w5XD2Nv6TeiwQHAskWaUsR1S6mQqiNtfrNhM1yiKn7D622wg0iIqIkX7smegTl9f1JsBZ0mvy5hhnuk1fvBXt7e0YGRkRXj8BIQS9vb0oKSnBN7/5zY/1XZsxYwafeJ2zJ+cAOOM+8cQTceutt+Kll17Cnj17+Id9dHQUq1evxrRp03DRRRfhe9/7Hm6++WY89thjeOWVV1BXV5fREOEFmLa2Nrzxxht48MEHcdNNN+GKK67AXXfdhcHBQV7xBut9CPt7ENvTj2TfCKcDAPBAG/YzPb6nX6hWXfMF0U3Eugacy1U3ycwBUqLoSBEb/ZsbYZtWWnSlGE1JM31VKEOjIIJaIrjdz5t6mfIizLiCRM8Qustq0L+pUWoAstAhbltOqFD6R2ATguF6X1o1707XoJy3yDOL6+9//zvuuusuKGpSAhsxzMadjJzJyiyDLgNGL/AxNYFGEmnDNN8PbeB6YiZPY8/HjCLiUqw4klZEOAZTOh5me840xYK567yAC3j5bQ/N4RgnNENNswtPmTIFy5cvx6pVq+D3+6Xn7OnpQWlpKa677jpcdtllH4pOyNmTcwD8ofcpp5yCn//851i8eDEfkglQLqu8vBz33nsvvv3tb/Pbf+lLX8K5556LG264AfPnz4cWTcAvKBHEtX//fvj9fliK5lac0SSVcbGshrjCOVUroSHe1S+NAeK8r0XQOtcFexaKkyI2r0xp48yE0j8i/fRXR8KyBI0QLiPT98cw2tyF+J5+aKEoDa3RDBqOo5uwFM0xduhIOgBKNIOqGxSdUxKhxg/SONumGS+jf3MjdblVNUHfHxNUHG413zpvFUabAzyoh3HGbh5FhfQrAQCqq6uxdOlSlJaWYsOGDVLObqbZaTLAukC4N96SVjUC4CE2LJdXHB/vnb5MbNMD+nJeMbUjy/ywaAgR1Qvpx+mdXOHSFCxLmDXy4sYovU1CCPQhBI2NjSgsLMz6HfjqV7+K6667DiUlJWhqauKZzCRlfei0s39lWejneH92Adi7/9//+3+4/fbb8dprr3FeFwCGh4exYsUK3HPPPZg4cWLGD9yRRx6Js88+G9dddx0eeughLFy4EPv27ZOSxVrnrYI6NCpVi0Sn2tvushro+2O8+jXjiivd0k2XvoirCCyjDreBqvckxYQyuB8pOyVXo7adNr7IiCZ5M4+YFs3/NUw+CUNULzBXm0hLSLy0cwKKtPUiRWyow7LDziYEKdvmJxCm1uC6YsOU6AvqipMNH8weLdI9pmli06ZNaGxsBCHylAoWvJ4tuMbbeGPAyGRiMlC7wz+ZQsL785/RGzFzRMqGkFURMjgCgGVZY54svMttqLWit7cXc+fOla5PJBIIBAIoLy+n74vzGj4MiH75y1/GK6+8gr05OuGzsj8/AOzdp59+On75y19iyZIl6O/v5x/0wcFBLFu2DIWFhbjpppsynvWPOOIInncrRjeKhgkegG4SakTgPGsFiKpL2QmMG2ZZDlwvq5vc2GBb1HwhAmBi7zCIZgjmBx1DW5thxmSrMON5ZfdaBRK9w/wEImb9skYim4RBHKrDexsWGmREEnTMkp1yA+Hn0hOQSKGwZZsk7cTB6BAOYM7rtC2SBopi4pm4eGPK1h3jhduIY6oHNl6ejYlnVASzCcsNMFfexqzLmZQGqVQqY1reWDrfkZERqXfhpVLmz58vqX/Eynz16tXojfk+Eojm6ITP1P78ArB3n3HGGbjrrruwbNkyzgkfzARjcdkmwcA7TbCSGk0Bcyy3DFBYNTvwThNswwQxiWs9jqtcJ2tbBGZSlXS17Fjq7yqGMjgKbX8MRjRJXW9zKxwaYAu0/TEEt/uortgB/cDyahjhBK9ERUMEa55RS7PrbLOSGqUR5lZAGw5nlJCpw2EM17Vgz+ub03TBLM2tvXQdRpsD3NbMAYVP/TB4epp7wnBPWN0rauhE44TsdAvrA7yyFGkBb7MNgFQJ00B1WWVge3jmVCoFk+gIaXt5ohl7HjebOH3cj/T6hAwJw9TQ0dGRBrKLFy9GWVkZQqFQhvu74UCBQEAC/IaGho8Fojk64TOzDx0A9u6SkhL454pmBd8BgZjHR66ooQ2ymDssc6CqiZsPRhraeLVHFAPxrn6k7BRCje1oL13nUAYCVWBasAkFaxmoTCm7gVXgnJN2RhrZpoXkviCIYYIQAnU4zJ10ytAohutasH7SNIRbAjJPXU/VE/3vNFJzyHY/Bqqa0L+pkdIJTmOP6CanIfzFK5HoHYbhPH9wu4/y4Vnm2olLDrd3TwZaQsHWrVslAAtrtAImxKUFNJKQJmDIAO1yvdnkbOKIIS9Yzpw5k/cSvE20TNVvpjHxmY5rb8yHhBLH4sWLoWnu54vlV+xL0unSYgVcWFjIQbSwsDANTHMA+7nZhx4Af+ELX8A111yDjRs38up0rPB0cdkm4c2m5L4R+ee6YNON7ennzSfucnOCapg0LbC8WnLSic1ABpL9mxthxp3JFBnkYbZFYCXdRiHlYZs4OLNsB0Z7yC46CgaMUujf3OicBMRMCsuZvFEjASYDW5FmEH8JZBvHlGl6x/pJ07D7+TIONpWVlXjmmWcwY8YMPPLII3j33XcFWVc6dUCBTjZ2aIaKvTF/WvXKOOBMc9dYJOkLL7yQkVpIJpP8/1taWmAYmpvyZpsccMX7MSAPa0MoLS3FhAkT0NjYyJt+rOLvjfmwevVqVFZWSk23goICRCMJbFzTimg0gYULF6KwsBDh0Rg2vOlHeDRHMXzG9+cfgAsKCjBz5kzMnDkTe/fuzQislqLDP0+2DzN5FVEMgapwaITdAUGTy+Io6U9uM67AjCs8BjJFbEkFMVzXQkFMNdLms7kgVQGiGrANE6HGdoneEEcGhXcHMNrcJaWW9a6uw2hzFyyFgly8qx9hfw8sAfSH63w0BEjICSYqdeOJmuHhOh/npwEgZadgxJI8+Ed0y9mmJSk7WudWQBkczQjEZWVlmDJlCpRoHMN1LVCi8TGBZPny5WM2vD5MM0zUCIsKBsOgbj225s2bhyuuuAJ1dXVZH6cv0Qrd1HjF+tJLL2H9+vVYsmQJ2trapBOHoiVw7733Qknq2LSuDYpKOemIPpyRZhg/fjyqqqqwaV0bpl63BJvWtaOzLQhV0bFxTSumXrcEG97055psn+39+QXgb3zjG3j66aez8rxLlizBr371K5x11lmYNGkSAoFAmn2YaWrFsUGMdjCjSfSurkOsawBEM6EMjkp6YTZLTaxAxUqbRUSKgAq4ub0MGK2kJsnV2kvX0YYeIbAtgiWvvIZvnnIaxuXlY+Kpp+P1lxe7KgVNloOxkBymWBiu82HP65thExtEN3iWcOtcev/6u4olbTOjPMxoMk0yF2r8wAkOcitlI5pEYHk1ANnVxxYPJUqoY8qtqIMuOyfLKmCL0Md+4oknJKDM5LpjCWrZ8hueeOIJ/OEPf0BBQQEuuOAC+Hw+roawiMXHMPVEW1BaWopIOJ5Wlf7tb3/jBofBRBei0Sga6now9bolaKjrAbEJVq9enQa+3/3ud/m4rCQD7KSBWdM3YnttAKpioLN9BKpifORAntz+l9ifXQAWebAvfvGL+OEPf4hHH30Ua9eulVxxYoVZV1eHY489Nu2xrr76agCAFk1gd3EZlHAMWjwJ39xyGEkVpgMqIifbOm8VlMFR2lBzsnuJogu8MG1iMUBlVSlryKUIQaStl4bY6Catqh1ZWWxPvwT6YmON0hK0yv7Tbb/F17/4b3gk73y8lPcjPJJ3Pr42/mjcnXcW5Wp7hqXqONk/Atu0MPBOE+VtDZPSGGID0eF+iWYg0ePOrPMXr8RwnY+nr400tLmV/VzKV7PKmFELTN8MAP2bG3nzkp0MRcldZWVl1n/npqYmD+DKI4CojbYVqq4glUrBMAwoioJXX301Kx9MgTQ7b81lYAqtzktLSxHRh/F+aAMGE12SUaK0tJRXpTtqA+js7ORVcSIZw1AiAF2zsGldGzTNQsuufmiahY1rWyXAHj9+PJ566ilYloWhoSFMmTIFBQUFAADDINi0rg3xuMKraF2zsL02kKMiPrv7swnALMzHV1IOI6aACCE4HR0dePXVV3H33XfjtttuQ2xkFL6SlTDjtMKsqqqSPqzXX389VFVFR0cHLrnkEhQVFeHCCy/EZZddhjlz5uCJJ56AqWhI9A7T5pmQRGY5l4sONEnq5YTbcO5UM0F0E4FlVEGRIjbV9kYSCCyrlrhiGtZO1QvEosE53WU1PNSndV4FTs77Eh7JOx+v5E/m+5G883H6sV93jSM8lIfK6GzTgrY/Ro0ZTuawpNm1ba56SOd+dd5AYwaO1rkVjmGDugK90j22RPneQNV7CIfDMJ3LslXABQUFSKpxR3rmVs5SjKVt8SqzN+aj1WmyFbqp0hCllMHVBmwxUO6JtqCyslKiIzRDwebNmyUtbWlpKWLRpAOgNH9BbI4VFBRAVQw01PdC1yxUrm2DptJm2qRJkxAOh10q4a12dHV1pdEI5513Hnbv3g2ATjO+9tprccEFF2DFihVpJwf2WJVr21C+ZBfeftOXoyI+m/uzCcBFRUVoEQwSbNm2jf3792Pnzp2YP38+HnzwQTz66KP485//jKuuugq///3veXW8YcMG3HHHHTBijrMtoSIUCiGRcANy2BKbTUNbm2HGlTTgZGoEBniMbmCh68n+EZ63y6IbGcAxBYWYJZxKpRDb088dd2Ilu//9TpgxBePy8vFS3o8kAH4p70cYl59Pj7FnCJaiI7w74OiMDd4EZLQCoxmYQSPZH+KB76/kT0ZgWTXUoVGkiA0jmkTY38PB1raI8zq2wIzSJlX669P45WK13DqvAmosgYaGhqz0Q1FREXpjLdJsM0VRJPvyUDLAq1HNUDGU7JKA0zA1N/PBqXhFp9iKFSskCRhrmLHK1TB02iBb2+qAXitWr14tHecXv/hFXgRUrnXBsbKyksc+6pqFTW+1Q9cs7NmzB4qiY9Nb7YjHFFRWVvLPGquUdS171ob4WDv+yRVwToHxie7PJgDzuW7FZYiHwrj//vsxZ86cD63rlYAhS+A6lYa53DCrRG1C+E/tUOMH9PIVNXwEELMMU5MCzVygNl1dui6wvBq2aVEu2QFGxpdaCRWxzn535I8wA667rAYTJ5yeuQI+7ut0qKZt04zipIbkvhF3MKcnoY0oOtShUd6IC7d4K3eDy+a4eUMx+GSN2J5+2A4ApcV0ZhhnJL6fALB+/Xrceuut+MlPfiJJrwoLC5FIxtAb80nmDCYj2+uAqHh7RU0irA3xlLDS0lLO1/bGfCA2vb9h6pg+fTruuece6KZOrdD/Z4PYVJerqioHw3hcgaoa2LSuHUrSgK5b2LlzJxYsWICHHnoIf/nLXyRwpBWwhUWLFkmfterqapSXl4/5eWTV7cY1rRgcHOSX5+XRgbLeVVNTkxEM/xFAefnllyMWTWLDm74c7fHJ7M8mAOdl+IAVFRXBV+JWxZZl4c0330RjY2PGqpYtObnLDVAXLcisYmO3YYYFlgkhh+rUgGgG51JZxRlq/ABEM6imVtD2Wg4g08dSkUql+EBOakdOce7XNiyM/L0Nw3UU+F9b9Dd8bfzREgf89S9+BX+67bdCbq/OjSFiFU2DfDSk7BRvGDKVBZtFp4diIKrB58QxLji43Y+gM1DU5aipYiTZP4Kwv4fOphPkcwd6zwFIFbqvpByxkVEUFhZi8+bNwrj7Vt7Uqqys5BQAA+BkQkVnWxCKYmDhwoXYvHkzbJtAs5I0v8E2YNgq/u///i/tuDSNGirWrFmDHTt2uDTBGj+SySQ624KYNX0jKte1ob+/H/F4fEwwHWuJlW5tbS02b95Mj0E18fabPkSjSc77RsJxXH755WlcOEBt3eL3Ij8/H7feeiviMQUb19CmYCYt8cHsL3zhC7j00kvxzDPPoKmpCalUip8gcrTHIQ7A3i1WxVqUAu7AwAB+9rOfZbytMkqzG0RJlTh3TbQMswZSpK0XVoLyvgzMRA6VNZ1G/t7GNbVsQGaK2BioagJhTS/nscWAdn9JOc3zFdQFlkIlbmKjzLYIwrtp3sLdeWfh9GO/jnH5+TjjxFMx52e/FZpmLQjW+xDvHpSqaNN5DEvReFXM7MRM/dE6bxXaS9chsLxaUnIwjTF7DWz6hjhx2Ywr3FjiVs3pDS/2njPTCp9X51ToTB/MmlmDiS6QlInemA+KmsTixYtxzz33IB5XsXFtK4hFoCQNbFzTCiVppgHdgZaiKEgkEmhtbcWOHTtgGBbnddljVa6ljbS1a9fCMCz4dw3AstzcimzPxy5XktTUIXLCLS0t/HYLFizAggULpNu8/aYPw8PDWY/7mmuuwcyZM7FhwwZEIhHpvttrA0gkNGxYc3C64e985zt48MEHUVlZCVWVJYSmaSIRV/F2rgLOAXA2EGZf2P/6r//Cu+++CwDYvn07zj//fH47Wi2Xc9CQ4xdd+qKrq8sFZoe77C6r4coBf0k5pxR4M+qdJsQ6+2E4eQ0c9OI0GY2Dk6IjZdsI7w7QmMmScpgxBUSjyoREzzDNDy5eCXVoVPpJzxxsVlKYsKwaILoBM+bm9ppxRcqZGG3uonpd20a4JYDhuhZpvhzRDN78Y5Zj27DQOpfyuoneYaQIwcjf27gcjuhGGu3Agop4boTjnvMur/NPpEaG631I7I9I9EJpaSlWr16N1atXu3paxeDSrr7eiKSblYBuXTsISQ/48YJk5VoKnqZJOJD6mwdhWTYa6npQvmQX/LuHoGsWGrb1QBPuQ4jNOeBNb7VLjy0ex/Bg3AFkSmmYJuEArQonKl2zsOFNPzTVbUBu2rRpzNcAANFoFHEHKDXNGLNqPfHEEzF16lS88cYbEsi3t7djw4YNME363LW1tZg4cWKOA84B8MHv/Px83HHHHQgGg0ilUnjxxRdx/PHH82p5qK5FssdmWmNxl2zZTrC66HKziTv4Uhxq6Z1mLDbwiGnBSqoycMcUENNKn7Axt4JOKe4ZhulQB9wiHVdgKTqUfSMU3B1lRejddpqk5miQ2ZTk4HYavclcbyMNbYh3uQFGYtYxVW8QHmtJdJqG1l66Tua2l1U7r3eV9HqpM5Ce5Fh8ojR6qd4nuQKNWBItxSuRDMeQTOqoXNsKXTOwYY2fA51l2dj0VjsMgzgg2gpNNakcjVfEBiJhFdFoFOvWrcPcuXNRVlaGnh43A1hsoEXDqqQ0YJUwe76Guh74dg1KgNvXE0Y8qnNAFpdpEOys7YaSNFC9sQN9vWFOaXS2BTm4N9T3HhBgx1p9fX148MEHMXXqVFRVVQFwgNypgC+99FJcffXVmDdvHlpbW/n9RkZGsGzZMtxxxx047bTTUFRUBF3XEY1GUVhYKI3pyu0cAH+ofcwxx6C4uBiWZSESiWDatGl4+OGHMTQ0JFR8BzcZwz+3PC1DWIyjNKNJyhE7Ay/5HLWkxqMo3VE9q/h0CZrF4HMaXLpkWiCa6TbKnONlmmMW1h7v6udNPTE4h/HJzETijawkqsGfl1Wj7mW0Ggu3BKSAd1Hr3P9Oo9tM5BM+tjgyOHr84d0Brgvu39QoNebY8bSUrERif4QbC8444wxUVVXxvI6hbS1cidBQ3wvNU0GKK5VKob6+HqZpwjAIOttGYBgEDXU9WakI0zR5Nas51WxfbxiE2LziZcu2bV4Bi5SDpppoqOvllASbPsyrW9WEaRI01PfCsgh9jp4wDM2SKukPu8RjGOs1Zlutra146aWXUFxcjLffflu67u6778ZvfvMbPPfcc7mqNwfAH2+feeaZvNnhVUyEQiG88847kiRIBIix1BVyHOUqnpImNfI8UZQsUcw2LYR3BySpm9I/4gbiODZgohg8M8J2DBte/S4bUy8221gD0TZMDvzi9WKGBQBeuY82dzmKCzeSUry9+/iWm23s5A6zXwLi+8zUE+y2YmbwnmVVvKEm/ntxXv/5MiTDMUQjCbz9pg/JhIZUKsUrSF6dOiAUCoXSxgsxmsJLDYjLNAlCQdr8Mk3KJ3v5XMMgMA3CK9+Guh4k4joatvXwata7RL6XLZcTNmgFn+E24lq0aBFuueUWFBUV8V8O3sdn1fpYrxGgAwdqamqwbds27Nq1C3v27JGyLryLHWskPLZlPLdzAHxQe+nSpQJ4VeDPf/4z8vLycNVVVyEWi2FkZAQvvvgiWudVoL10nZTlkG2JwT5MuyuaINhK2SkQw4Q+GoMZd6IrTXmMPMv3dZtdMj+qDo1itFkGbTOuwDZMDnB8wKdto3d1HbSRsBPa7iaRsRB3MSOYKjUMbvpQhkZRf1cxYp39sBQdxLAyGi043+ucBFrnVQDILPNjGclGJAFfyUrERkazfrFFzlH8/5///OdQVdOlIDIAWDBIwdA0CTTnttmaY6pighAbe7vDqN7Ygc62IDauaaWg2u46KzvbgthZ2y1RDYE9+yX+OCOAeZ6bUQ47a7vR1xvOeP9HHnkEALB161ZMnjwZjz/+OGKxWNrJRXx8sWnIViKh8cZZJkXElVdeib179yKVSqG0tBQnn3wyTjzxREycOBEvvPBCLnviH7cPTQBmlZWvZCXMhArDMGAY9Of2+++/j1NPPRU33XQTHa0jVH8Hoim4Bbd4pVMFGm5co2ZKoT7dZTWwFA0pQrW64hh5Rht4XWrDdS1Oo87gduTgdj+Iqsu0gmFKwUCWooOY1DShOy64cEtAoElWcerEq4qIdfULWcLpI4bk17TFtTM79EmmdDTbsBBYUQ0lGv9YWQa///3vxwQ5tlTFRENdD0ynsuXHbglNMyfsxjAIhgfjDmhbUJKmVAUTYkNTLU41MAMGIfSxD5YCEBUVpkngbx7kDT7v7Tata+ONuNWrV+M///M/+fWGQU8u7NgBSAMIALlCNk3CdbxXXXUVli1bBoDORJw0aVLG70okHMemt9qRiKu5CviT3YcmAOfluZXVzTffLH1Yf/GLX6CgoICHtjOJVXC73zEoyCBsG0xDq4FotBFnOxkLonZWd2Rv4mh3FkXJKt3gdpo+xlQRVlKlY4SKV8JKqBQ8LYLACtr0indRtYVtWtIkjJSdghFJYPezy4STgS4F8zAQJpqBFLHB8oC9SoaRhjaEWwJu8tmKGun1Z6pwRXqHufxYk9NL/bS2tmL+/PmYPXv2h/pyFxQU0Ao4i8TMy4syc0NnW5BztP7mQc7lqqqJxfN3QlMtCcwYeHlpjkRch6aYEmB6q/BskjQJ+IXbep8DAKdZstEKrFIfi76QKmT+XvihKAoMw8ATTzyBL3zhC2O+16Ojo6iurv7Uv7efs33oAnBeHp0j19zcjFQqhfXr13MvvilwvkQzJWsx/YnvcsP+uW6+QYrY3FThxljS6pJxwIwasIkL0N5Kl4F4ct8IHaTpjLlvnVdBK2Ah1YxoBm+EMY2veL2laLQirvelSc5YtR7r7Kfa4hYqiWN6Yza80zsqCaCcNKug3YS1Jq4LFjniTHnG/nkVXOLEQHksKoJtduIsLS3FxrUy8DDnm2gYcFULvTANAk0x0VDfSytZUXZmEPT1hLPoiNthOqaIqdctQeU6RxkhNOEMgzbVGIgSYqcdw6Z1bVxB4VVL2LYNVTEzAr6SNDzBhOkAACAASURBVPkJwbsIYfdzq3/2HJpqoLa2FrNnz8YTTzyBXbt2cZWIrlnYvXs3zjzzzIP6rlRUVGDPnj2f+nf2c7YPTQAuKCjAokWLEIlEEI1Gcfvtt+PCCy/EHXfcIYNESTkHDlGZ4G12ceXBXJmrZStFbARWVMMIxzFcRymJgXeaOECLYM0lWibhY96JbkpNPuZC8xevpJpgJ2WM6NTwwULjA8urKf2g6pJqgtELrAK2FI1GUrKpypoB4jTv1k+aJqWiUT20xXXEgWXVvDJmE0G8YG0lNPgd2RzRTXQtq0ZsZBSTJk3CnDlz0OI4GJnxYqx/t3hMcUJxzIy0g2XZvLIVrxO5XkYziIBavbED0bCapiPu6wmjozWIxfN3Sk254aE4bDuFhjrafPNSFUwpwYDRjaHsldQSluU2+9ixbXpLBvwxK1sH/MXLKtc6oK1TsA2PxjB58mT3NrqOOXPmfChp2cyZMwEARx999Kf+/f0c7UMPgAsKCpAcjY6parASWtqwSaaP7V5R45F7rZLsxaLNl62BKjpHjlaIJnXWZQgqz/b8bMIGr3pZUprDBzPbcyqVQritR7ptyrapssEiUEfCUggQNXLIofNmzDFvOGYR2ahSwY/JNomHH3eNG2JQO+AG9Ihpb8888wz/92BKB1YBZxP7FxUVYUdtIKteloFPPKqncaleMCPE5tWuxkwVqkmbdQ4YApRjpQDZDkO3YNsprpBoqOuBZdkYHoq7Jo3mIYnfHR6MC8YLyuUqioHOtiAIsSUKoXJtG/p6IoiGVQfwMzcOs70m8bLKtW0wDYJZ0zdiw5t+fr2iKDjppJM+9PeGRbZedNFFn/p3+HO0Dz0AFjMj/HPLUV9fjxdeeIHTCqzidcetuzkFDHAyZRx4K1j5Ohd41GAYEX/PmNI2Lw/LzA6s0qRSMTdnggehO9RB94oaSWbm0ikGAiuqkXL4X3disl9QelQ4Tj9NkooNVL2HVCoFpX8kbQBn+ut13wtVVdPs2978X6/SIRZNYmdtNzRVR2FhIU499VT87Gc/Q21t7Zh6WS/4iJWhZdlpCgGqEQ7CcBphgT37YThUBHPNNdT1oHpjB0b3K/z2fT1h7O0OIxF3aQTTpNI0kd+tXNsGQmwsnr8TfT1h9O+NwDLd6xvqex2ApxSCqlCdsG3TY/XqmxmwGwa9Da+WHScdawBWrqMnoYa6HnS0BaXXPBbXO9Y+6aSTAAD33Xffp/4d/hztQw+AM1VcRUVF6FpWLSSOaViwYAFKS0vx8ssv8w8vG3TJ5p8d7JKrYx8HxWzSNnGisKVovGIWgV+ax+bvkZpwTPtrxhTJ/EF0E/V3FafnO8QVt/EnxEdyq/YK2bzhTr7I/ivimWee4RVsQUEBVq9efcD837w8eoJs2Nad0VGm67pU9QWDQeTn5+Odd97hALXprXbeUMvUDPM3D0JJGhycLYsaLQyDIBHX0dcjA6thMOMEBc7F83dC12ijTqQwWLXZUNfLQZBywYQ3CzXVhG2neMiO9yTi8rkUZPkJwnCjLindIeuTRdBn/PaO2gBXTqxZswaJRAKrVq36WN+dUCiEl19+OWdH/uT2oQfAeXnpSWoFBQXQk4pkUFi0aFEaVZFJUpVtsUhLNpRTbORRE4ZbFTOAY0sK3iEE8a5+2MTmvLBtyiYMfTQmASRNTjNBDNMdhxSOY7Q5QC8LxzHy9zak7BQ/Vm9VzxuIcQUjDW0SB53oGRrzBMLWuHHjpPedDd0ca/wQVTfoUhXJ5GOapkFVDP6zfObMmcjLy8OXvvQlrvkFwCtDBqqMf61c1wbfrkHsrO2WtL0AVURoHmDdtI7aixnXG4/q/HqR903EdQwPxhENqzAMgj3tIyDERqAzlCZ16+sJO/I1m1etTJUh0hHZGoLe44uG1YxNPQDo7e3FxRdfjPPOOw8AcPvtt4/5vfjhD3+IOXPmZAXWzZs3o6OjA7GYgo1rWnOBPB9/H5oAnGkXFhZCDcfROm8VtGgCpaWl8AmA3NfXJ4EQW1VVVTj88MNx2GGH4ZprrsGaNWsAUE1wwKmqaeYvkfJ+iWYg6OQeMJsuW8P17kgfohnCANAK6PtjrvNsbrouV4qydH5Gp2zbGYdEnXRqMCzQImNrmxk4i4YQd1bcKpix5JiUxHnnnfeR/i001eR8rm/XQFbJ2bXXXosjjzwSpaWlaU0pVk1qmsUrUxagY1lEqoxtO8UrTFWh3KuqmJLTjlWXIhVAiA1DdxtpmlM1VzrqBf+uQcSjOq+Axeo6FtHo63SOwbLoica/e4iDvtgQBGTVg6ZaMDTL0Tn3Sg1IAFi6dCkeffRRPl/u1FNPzfqel5WV8fcjnMXx1tnZCUBOacsZMz7WzgGwuFng9X333UcjEPdHOJfp9/slbrOjowOBAKUiNm7ciIkTJ/LHmTp1KohipE2ysAkdLSRyq91lW5DoGc5o3TXCcfRvboQRTkhUQqaMXbaYkcM2TETaet3jEAZ72oaJwIrqjCFEXp0uM5MQi/DMClYZj+6m6WosptPn0AveNXfu3A/dPa+trYVlEi4fO5AiAIBkTRaBYuOaVoRGkujrCUuPIVaStk1laf5dg25Gg0M/eHljJj/zNw/B0C1Zi7uuDZ1tQSmBjSkmJKqgrlfigze91Q7bTiHQGZJODH09YRh6+gmOga1lEgSH44iEFeys7R7zPUqlUujv70cwGMT777+PuXPnYvHixfz6bMB61VVXSVnESlLPRVJ+MjsHwOJeu3YtBgYG+E9nNhDRu5566ink5eXh8MMPxwMPPIBYLAbLsrB48WIpSwKQG1Ks8cUUAYwqYCoDQOaLWTaw7DTTQEwiheV4F5v1xhp0geXVMMIJSVnB9Lkil2ubBH4npjO43c+PZ/2kabSB56SehVsCGG0O8MdKEcJPNC3FK/HGG2+kHVMwGMSjjz560NzhvHnz+H2HB+NIJgwOxt7mFCA6xiypIUUDe0zOqTJqggKJ+xM/GdcRHlUkJUVDXa/0+Cy1rHpjB23UWQRtviGHHnGVD6L8TMyUYGltYqKaSB0wYG/Y5la+nW1BaKrJw3sYZdGwrQdKwpD4YMPJqDiQEy+bQYT+6mhFIqHh2WefxaxZs6S0uHA4DIBO9shNY/5Edg6A2T722GNhmiaef/55FBQUSJUBWytWrEAymcSRRx7J73f00Udj5cqVY37g2WJKikTvMEKN7TBjyTS5G2uumdEkpw24w43YHKyZXjfe1Q9iEp7Ta7PoSmb4cAwgzB7MQJwtrwHECMe59Mw2LBCN2ppZRCatrml0ZmAZTVgTjSBqmFZF55xzDgYGBhwNMDOIGGg5QPYD28888ww/RlU1MTwUh6qaEicqLla9dbaPSGYG77LtFAc5MSktrbHmKBrYaqjrQdPOvTxVjZ0IAp37OTAyc4ZpEkTCKuJxPa06HhmKo683AttOUepgWw8sx4rMbmPoblDPrOkbJcNHPKoj0EG5Ze9j9/WEM0r0vIDLTyK9NLz92WefxTPPPIMtW7aMGfz+3nvv8QZjrvr9RHYOgNm+++67AQBTp06FkkEnvGjRIoTDYezevRuXXHIJbrjhBlRUUDfXh2nOsZ/4LJ3MG4ZuKRrP5+1dXSfZh8VweMYhs2kZ7PkN5/YiqK6fNA3Beh9PVks/HmdqsmJgf3OXZwad24Bkagx2PMzIoYZj2LO0CnpSkRpsTz/9NJf8+UrKMVTXktFwkamr/vjjj0vHGYvF0kwS4mKVJQNTplyQrncqZFZhMu2vZVLOVlQYqIoJf/MQb6aJOl7G46qqSUdICdcbBuHZE67RgnK2jDNmEjdR8kaVEZR3NnRL4prZf1kjMZVKIdARoppiR6OsC5W3qPIAMmugtTFs3BnfW4+tOxP/m1NH5AD4I+9t27ahra1NmrjcUrwSAwMDaeAp8py2EwPJgHSsZRtuZgOrRJlcrP6uYrTOreAZD+LEDfn5V/FcBzq7TZUab8rQqKu6iCZpSPpcGux+sEs8ztZ5q/D000/z60Rw9/v9PPO2q6sLt9xyS9oXkkn+EvsjSIZjkvyP3UZRKKAlkxoWLlyIm266CVu3bnVBTbewdetWTiGMBRqGQVwnnAAw3sqWT7GwKKdLzREjMHQKkizhzN88yOMnZ03fiFAwKbnadM1C9cYOxCIaCLFh2yn4mwclamF4MC5JzJSkwatm8XZ9PWFeFff1RPj9hgfjknSN0Sv+3UNpUz4yZUd4T06VQnXNTmau4cSlM5h1mnPpa1thmfTfgFXAX/3qV3HhhRdi9uzZSCS0nDoiB8Affk+YMAEA8Nhjj6XphMPhMNrb2yXw8c+tQFVVFa688krU19dnrYB1XZf+zlRZMkdZ2higA8i80uIrneff/ewyJyXNx2MxR3d3SUB+oEU0U4rSZMFEgKw/XrRoEcaNG4df//rXiEQisCwLc+bMwZe+9CUJhDPFSbLri4qKsobJeOVemYCXcaki0JYv2YVKQaPLqknGB1OzQwqEENpIc6pWUSusaxb6+yJcjaAkDPk2Tkg7A1RmhvCaMdjxdLa51MjGNa3o6w3Lpg3ndpvWtcMyaUU8ul+RTiSRsHLAXGOxas60xF8RnW1B7vjzhvqwRiI7yRzoxCc+dk4dkQPgD7UfffRRAMBpp50mgcbFF18MAJg1axb9sic07C4uQ3I0ira2tjE/jBMmTEBeXh5uueUWfhkLL6fSsnQeU1zZnHUsYYxPZY4pGNraDH005kyy8MuNvD39ki1YfvzsTjyxESfelnHCrAHY39+P2tpavPXWW7As+gXdt28fioqKDurnaEFBAcKjMbz9pg+RcBzFxcX8OHTNkjhdMdRGbDqJ1mPDIBwcWVPM3zyEvp4wUqkUImEFi+fvRMO2HqRSKcTjOmzbloCpemOHpEJQkgaGB9MpEK/KgnPP23qgKq68jLr6LGGKB7U8M5MGO052P2JloA/WtaFp514ukRMzLsQpHozf1dSxhoO288qcURWiI0/X6HQPNsop23LzNQysXLkSEScwP1cB5wD4Q+2Wlhbs2LEj7XJWGW/cuDHrh3Dr1q0coNn69a9/jby8PBxxxBFYt26dBGKcbshQ2W7duhVLlizJ+lyAW/kOVL1Hw92F5LPdzy7DaHOAUxBimpsXaMXmnnd5wV8EdO9QyNdeew319fVSlcwe40Bh62yzE96TTz6JeDyOeDyOF154AQAdBilWYGIFyKs0z2gggIboVG/soE0xg2Dx/J2uLlg1peqVEFoBV66l3O/wYJzLvBbP3wnLsqEo8nw5MWzHO1iTSdRMk8CyCDrbR+gJQ6eAbpn0eKiMjTYCTZOkUQ2AC5hUCUE5ZeaOY1w0n2MX0dJPWk6lm616Zc/nTVtjLjxNNbjc0ru8FW+OA/7QOwfA55xzDgDgN7/5Tdp1F154If0wxpJp1eLy5ctxxRVXSPpI+sE1MHnyZBx99NGoqanh0wuyhfW8+OKL/L5M6D7WGgscxcdNEVsagOnet4I33OSBmTJAK4o8GJM9Z1lZGU488UR+XXl5Of/CHXXUUTj77LPx+uuv8+bbgRLO2L7iiiugaRQgk0kNy5cvBwBcdtll0HWdKgV2DUhgzDhh70QM9jdLF7MsglAwISkl/M2D8O0ahG/XAAe+zrZgGpCyStJVSow4Az8pYHKgr+vh+mEadWmiqyOEwJ79AMAnJtOxRSM8oIc160ZDCmzbHbPEgJMQG5GwgmTCoJWuamHx/J2oXEsVFaL8jcnzmN25s40Cv8gJZ+PGN73Vjn379uF3v/sd/vznP+Oll15CS0uL9DnwLvbLJVfxfuSdA+Bnn30WhBAcf/zxadf99Kc/BQAOJqxqvfLKK/Hkk0/CNE2MjIxw6Q43UXiGdjJbcvawHnFuWuaffN3d3RkvZ6Pd02zEc6lrTqQevLxyKBTiDTxvRb5p0yYkEnROnKZpPOVMjyZQUFCAO++8kx+3t8rNlLdxoH+HP/zhD2n5vtLxCD/vbVseyeNGOqZHPwKQGlmUEyZcS6trcnav1+rLquzKtW0IdIQkwG3cudcBaOpKC3SGZF2vw01zHlkxOJ9t6FQBwcB2b3dYqso1J89CVcw0eRwLGmJ8tfh+iG4/TqP0htMC5iVAzhDbyf72+/3o6urij9/c3Iz169ejt7c3V/F+/H1oA3B+fj76+vrw9ttvZ7x+9uzZLqDNq4AWTUhf/KVLl+K73/0uDMNAU1OTZF0WF4tjTPRm1liKkzMy8a4AkEgkMGfOHPT19SESieAHP/iBC0DeKRNig07gEzPxykZcgX9uOhdsGG5EpZ5UhNdWwfldphbJVOV+2C+nlwv2UjEi38t0ri4HaeKtCh9MgyAW0aAkDeys7U4DNF2z0L83wqvGeFR3dLg2f3zWbGMgSuVaFKACnfLsN0JsxCIaHUW0jVaylQJQsqYai75MOLrg+poAH1+kJA3omoWujpDkiiOOztdtRBpcB+x1+rG/CaEnla6OkGRj7usJywA7RkNNMpp4FBbedcstt2DixIk44ogjPvXv8md0H9oAzEDs1ltvlS4//PDDcf/99/MPWllZGWbPno2lS5fyy6699lrk5eVhwYIFME0T119/PeKhMJepTZs2jdMbRDEEPW+2CngVr4D7+vokMO/o6MAPfvAD7N27F+FwGBdccAEf2AikV7aiKsO2CKci5s2bx6taL2irqoq+vj7+mMuXL+ePG1heDTUcg69kJayEhuuvv/4jVbkH2iJo/8d//Ac9TkHlQEfFR9KruXXtiEZomDrTx4qUw8Y1rehoDTq2XkuqJlnFSCybP5dhEB5ys3j+ToSCCRBCpPtuWtdG72MSqTp/9ZUlOOnE0zEufxwmnPLveOWV12FZNuprAs4suR7ZbefkS7BqnOmLTZNIuQ+GQWCZNlclAOljjAzd4pWv5jT/lKRBm4oHmJTMlnjSOhiNMFsDAwOor6/HG2+8gaeffhp33nknfvSjH+G0007DRRddlKuUM+9DG4BffPFFJJNJHHXUUfyyyZMnw+/3Sx+uiy++OI2fnThxIk499VSYpomFCxfiW9/6FoLBIDRNQ2FhIcaPHy/d/mDSw8QlTstYsWIFhoaGEA6Hcf7559P0tigN57EUHUo4ht3FZVAjcQCQAtHDLQEQVedUxLXXXovnnnvOI2OjsjddqPBHRkZgOcHvVkJDb28vPwGxE9Y/6idoQUEB1wGLKgeRMwVkKzEALg9jBgfJiqy7FmVNqAL9zYNOYlovD1tPxnXKo+ryUE7XZOFaokdDCjSVHufLL72GY7/ydVx+YRF+PuUVXH5hEY479uuY/uBzMB2gLl+yS2qcMSpk6nVLEOjcD8t0Mh6EJiA7ARCLYG93GJoqAyNz4fX1RKSTUmgkmTHPYqwlnrQ620bGbOAd7NI1Cxve9Oe44vR96ALw+PHjEQ6H8cYbbyAvLw+nnXYaVq2i4Njd3Y3rrrsOu3fv5tKqnp4eXHbZZTjhhBNgWRaef/55LFy4EKZpYsqUKRgZGUEwGMS5556LvDwaJsNWTU2NpJ8V10knnYRwOJxVFiaaP6ZOnYq8PKqd9Tsuut3FZSgtLeVAOGXKFGemHZ1FZ8YVqozQXB3whg0bEB8Zxe7iMmiROA/mcSto9znb2towffp0jI6OQtPose3cufMf8kUaN24cHn300Yy2YDESUgxB7+vN/FPZNAkP1iHElsYNMbBlQToMXAlxG3BsTL1Y8Xa2BdG/NyJYog2kUlRPbBoEE075d1x+YRGmXreE78svLMJXvnwyGup7qdTMM3gzFtGgJAxOYbCMB6Z0sByqQlVMzgu7JxdZjidmTRzIrMKeLxNYilK5sQaCAkBpaSkef/xxFBYW4sYbb8Rtt92GwsJC3H///Zg+fTqqqqq4vjinD07bhy4AT5kyBQBw4403YtasWdB1HYqiYMaMGTjiiCNQUFAAwxmwqUXiuPTSS/l9GVADtCkRjUbR19eHb37zm8jLo7ZmBmLKaBTTpk2DzkbIC4aI1157DePHj0cqleIj7VvnVeD111/HDTfcgEgkwivVlpKV/MNbUFCAeCiM3cWZf/5PmzYNtkmnXphxOsSzr69PsveGw2G8+uqrKCwspFSCU0GnUimhgq7Bu+++i7y8PJx88sl4//33wea7fVK0Q15eHr785S/jgQce4I1GyYDhBKzTk2HmacKZ9K6mSfi4d8sksJ2f8xvXtEJTqRzMm6OgOBZixoHGHFnXJidakmlnxSpzeDAGVTHRvzeC/Pxx+PmUVyQA/vmUV5CfPw6GTtUY0bAKQuh/WVXMwJ7RH2Jl39cT5pGckbDKR9CLzTSxURgJZ7fDiycXxVFVZAJqXdfh8/mga3SAqaGbePHFF1FUVJT1sdn92tvbsX79evzlL3/BAw88gIcffhhRjz4417zLATCqqqoAAPv3U5nQ0qVLcfLJJ/Pri4qKsg6LvPzyy6UPXkdHB89aPeqoo5BIJDhw+kpWcimPz+fDa6+9JqkHZsyYIakglLD7M+2SSy6BGqEZxWZCxYsvvsgbHgf6ECeTSbS0tKC0tBS/+93vEI1GsX//fvztb3+Tjn3JkiW44oor+GM9//zzEodcW1vLH3PGjBkfWl421j7ttNNQUlKCeJxSJ3V1dXj00UeRTKjobB9JSz1j0ywq12WQUQl6V0ZBiBWz5vCqne0jXEXAlBMsf0GUoLGmHaM8/LsG+SggViWyvOBN69rR1xvBN74xMWMFfNKJp8u2Z4eXFd1y/Xsj0FQzDeCrN3ag0rEEp4XBvyVnQDBKhVfCQoWcTd0xlqOOBco31PUg4mQEs89+S0sLAODss8/Gf//3f+Oee+7Bc889h1WrVmHXrl0Z5Wv79+9HS0sLkkk9Z1mm+9AE4EsvvdQNGI8ruPvuu9NuM1aT6aabbuIfqubmZpxwwgn8ugULFgAANMbRJjQ0NDTgggsuQH5+PgYGBjg4736+DFVVVbzh5itZidLS0rTjmDNnDqqrqwG49MiBXqPP58OaNWv432eccQba2qgEy+fz8eNPpVIYGRnBT3/6U+TlySee1nkVaGhoOKj35GB3QUEBFi5ciC1btsC2bViWhaVLl+J73/sev15R3PhGLyhkDNl5q93NNnAqwlnTN/I5bd4AH8bd2nYKyYSBRJwaK/p6whykOlqDaYqGynWUCgl0hGA4HLCoiFiyZCmOO/ZEiQP+yjFfww++dy8HPRZzyfKGmdog0Bni2mJ+EhCkbO70ZYPnOvT1hPmJhTj0RSKuIxHXPVV6XAJtTTV5Q3EsNYSSNKRm4fLly3HvvfdKt/vggw/w9ttvY/v27WhtbcXAwMCY2mEgZ1kW9qEJwE888QQPKN9dnL2Sy1RlFhQUSOAqjnl54IEHpA/a3r17ce+99/Lrf/WrXwEAlNEoBzFOARwEqF166aW8Qbh582aceeaZWW/79ttv47333pMu+/KXv4y1a9dKx/j+++9zM8maNWtw7bXX8uMxEyoqKioO+J4c7J48eTIdKeR88bdu3YpTTjlFus2iRYuEZLLsGRCm6RonAHcMEav4Grb1uBkQznQLQmyYJpEmWLDHZpSF6YTAs1xhHoDjaGtZBdtQ1+PGPzrPZdspTH/wOXzlyycjP38c/v0bE7F48etStcx0zLGIJh0HC+EpX7ILgY4QBeUOmrLWvy/KG3267uhzmwdRvmQXH68khsAn4rrk2mOh8IwrZzpodhLItLzDRTPxxWw1NTWhqqoKFRUVePnll/H8889j5syZuO+++3DbbbfhmmuuwUUXXYSzzjoLU6ZMQTgczxk46D40AfjjVHJiA6x13ips2bIFeXnUNWc6nLERV/DQQw9J9/vKV76CUCiEurq6jDPpDhbUDjvsMEybNo2H35SUlOCYY45Ju93ChQsRCoXSLs/Pz8dTTz0lfYFeeuklPPzww9A0DdFoFLNnz0ZRURHi8Tj+8pe/fGLv+5NPPolKj9kiHo9jy5YteO655/DYY49REDSI3NH3ZECw+W3ZsoEpyNhcdkYcK3GlIx3zBs8AkGawaaqJeFzjUywCHSHaBNzmzpazLDqKiFWIfb0RflxiVZxM0EadoROuwBDTz1weul2aI8cTz3YNOo9FJFqGnRQsyzmpOCcgRqdQrniEn5BY/CRPRPNoqjMtllOhqu57fNVVV+H666/nf7/wwgsf6fuX44DzkHeoAvDH+RAUFBRwBYEeTWJ0dBSHH344HnvsMU4lZKqq//rXv8K2ba6S+Lj7+OOPx4svvkgbeMEg7rzzTuTn5/PrZ8yYAQD44he/mPH+119/PZLJJP8iFRcX44wzzsDWrVsB0KkHuq5j+/btn9gXRTRbRCMJ/PGPf8T8+fPx7rvvwjCoA5CBoK673Kaqmu5gzbV0sGYmagGglbCuW1JlGQ2rvKomlsz/sgpYVjy084qWPQ8zYbCTwGhIQZuPxkH6m4c4F0udaTbne1njriewX3q8TU5KmxiAw/hl0STi2zWIhm09/BiZM86rCPHvGuTDPl3Lc5AHC3W0BtMC6zNVtolEgmvFvbprth566CHp74svvhjnn38+Jk6ciFNOOQXHHnvsQZkzckB8CAPwx9nsg8M+iP/zP/8zZlV97rnnwrbtj1QtHGiff/752L59OwCgsbER3//+95GXl4fbbrsNAKRZdd59zjnnSBbnWbNmIT8/H3fffTcUReFz3j5JxUM2WmfmzJnYs2ePBIIAjUlcPH+n1HQSoyC99ERnWxAdrcG0ytIw6JQKJUkbXY0796KvJyLdVwzXMQyC/r6IZClmIMemYIjhPppDIfBpx8LcOEZ9+HfJTURCiGBtpoYMkXdm8jlW9YaCCfh2DaB6YwcNkRcoguqNHehspxkTsh26V8i0kDOBdY0OAs20sr2/H2bZto1EIoHh4WEEAgH4fD40NDSgpqYG9fX1uWZcDoA/3j7ssMMwMDCADRs2ZAWXvDwa9r5//34c2GEp4QAAFytJREFUe+yx/7Bj+dnPfobBwUEAwKuvvoqbb74ZAPDjH/94zPsdd9xxXBECANOnT0deHh0JxCv6T0DxkG0XFBQgHI6hcm1rRh0r+zsbVyku9lNbUy1eWWqqOwFZDETXVIuHt4sj4lly2qZ17VyXm0mDnEqlJIokFEzwqpPlBzMtLwCeLTwaUhwLc/o4eUY3ePW9DChFF12m0CCm2GAji9h/CZHfO0JshIIJ+jqzqCC8mRHiosfQyv+Nfv3rX+MnP/kJbr31VhQWFmLGjBkoKytDR0cHD6Ma6zkO4WZcDoA/7p41axZSqRTP//XuW2+9FQCkUT3/qH300UfjmWeeoTkOjoGkoqLigNXFYYcdhpKSEv7FmDZt2j/EapxpFxUVYcObfunLPjo6mvVL6wUCsUpjGt7hISprY9VlX08YoWCCV4zU5RXE8GAM8aieQaJFE894qI+jvBBpD26GEGIyVcWEppk8+0HkqNnJobMtCEO3nCAfE309ERBio39vBL5dgwh0hpBKpaA4VAOVubXBJjYad+51Jz/X9XB5mWEQPhaJV967BrPSDF7gP5j3VmpIekLht2/fjgULFqCqqgr79u1Le6y+vj5UVVVhwYIF+O1vf4srr7wSN954Yy5NLQfAH39PmDABqVSKT0oW99FHH42BgQE0NTXxScv/jH3GGWc4kzoOPpM3Ly8Pv/jFL/gUj//93/9FaWnpP3z67Q033EBlZ2+1Q1UMzkEfCHi9ZgR2uQgqYjg7tyALFINl0Qpz8fydUnNsNKS4tIfTuBoNKTyXQRyWyapjUfLlDXinQzoVHhJkGgSxqJax2mUVs2gAYZpkcVgn46xnTd/IeVqxscZOGFT21pMm3RPlfLW1tejt7R3zPee8sdOA9LrtIpEIGhoa8Nprr+Gxxx7DTTfdhHPPPTdrDyIvL8cB5+UA+JPZlZWV2LdvHw477DDp8jlz/n97Vx4bVd2u326XkrJfUC+BEiwfft4v5l6MyigKFa6U1UQxN5K4NKaWKxCJmsiikUUieFFRQkrgQxZF4APho6RlSgGZLpRKWUq3S0tXWqHrtLN2pjPDc/848/vNOWfOdKM4Lfye5Alleuac0yk88867PO9mAOB52T+TXbmVBeIzzzwj+UCYbd3eYNwbhoSEYPny5bBarbBarTh//jwsFjsXGLZ9WCtC0xpGkIuE2oRc7hpmNrVzO0k2/FBa0uDbapwl+Tywbgh2DMv1ut0+5zS7t3Amb/kytlilcWWvoxrzDmaDIfLUhNpcfv2np6S+XodLUazLy6lWDE+wzoWODmmjBnvN5G8K0vd9e+c663boDroacX6IRfReKAS4L8jacubPn88fmzx5MpxOJ/bs2ROUe7qXFMLGjRt9fdL3If8bExPDvTLOnj2L+Ph4VFZWqoxg6nlhKZAYaA1qqIcy1OLBPIEdDhdqqoyKti52jNxjornRt2pIf0IaWXZ1uBQevMz8nA1F3PnDJJ3DO2RxMUNKjbhcyjFjl8vtax3zphNKi+uRY6jgKQKbN6ft8E7vMS8Jbn+pSIsY/VIHHR3ugGY86kWcvQG7XqvRLES45xQC3BcMDw/HnTt3cOLECf6YXq9HW1ubYkruz+a9tNr1df73gw8+gMFg4BN9gf4zs8iSFc7khazuojvbk+VCJRdL+ffZlgvmCcwi86xz5bBafKvnXS7JyvLObZMiL2uzOvkwBcvFyqN0j9vDjeKZsToTRea4plUoY5N5hVeVfcdWb5GxsxFjORrrzYp+aOZn3Go0Y/bs2ZgyZQqSkpI6PYfcrEg9xSnYJYUA9xXZZo2xY8fi1VdfBQCsWLEi6PfVW/Zlfi4hIcEvP6sWQQCKNADgbzfZGbrTNsWOUa8wcjpdftdR+0iwFfGF126jscGi/L69A3avW5k8HdHUYFG0lvENw7JeYqvZwReKsuuyQqK0r80XIQPSxB4bUWZdGkd+uoqLmVLqQd1K19lrIc+LK4prySVIT09XpNQef/xxbNmyBS0tLQCAoqIi5ObmKn5eIcA9phDgvmJMTAwAqXhVWVmJwsJChIeHB/2++gP1er2iZ7W9vR15eXk8+tInFyM/Px9msx2nThTBYrajoqLCrye4M8jzvOrotrS4Xmmczo5hHQwZlYrruFxuRZ9tWnIx7+OV51flPxOLoNURLhNl1hJXePUPPr7s9HZGqCPvtOQSaYLPO81nNTv4eiVmEm82Ofjx6jc23inRSd7XZ83pyxlrveF99NFHCr/syMhIxMfH8/F1h0Oa0HM4XDh58qRIQ/SMQoD7kvKP11pLPh9WJiQkwG7zFaUSEhIUU3FaNoU6nQ6tRjNOnSjyejBU+Ll8scr99evXYTSapFU+LlV06y2o2axOtLf7RHflypU4ffo0Cq7W8UhWPqZrMTvgaHcposlAG4ulfK60pVi+XdnpPZ4t4vQ5qRXDbJIsJhvrLXC7lW1i0li0UzERx3LL6kEUu80pFRVlYivPFQe0nFRN3DU1NSnEVI2tW7dizpw5ik9Fzz77LFJSUvg105KL0eJ1TQv2v7kBQiHAfcnPP//c65fbt9NjDwITEhKg1+sV/dBdpTl0Oh30ej0uGMq5Qxlf3JlcDEDayhwZGYnt27dzsZB7Osg/VssjzYSEBJ4akZuTywtuHo+HT+KVFksrjQLlV31DIFJvr7yLg4m+1epQ5LXlbyZyP2A/Ixyni/cOlxY3KIdAkku4aKrTMOyY1OOFmm1mWgU6uQ+FtmgXwagquMkLqA/xUEVvKAS4Lyn5QfSdX66g0j/C1GaB1dLOBe3AgQMICQnh++MAyafA4XDhRlE92u1OWWdEO5YvX45t27bxY3NycnjemeVh5e1jTFjlnQIsF6wGM/hZ/+kpNDZYFB/xPR4POjoky0j2eGlxA88NK6wq5QtHvT8n66rwjT53aBYa1SmbziLa/fv386GJyspKfP311ygvL++0iCf/3vXr1/H000+DiLBr1y602zse9qGK3lAIcF/yz5oee9jIImUpYpUmxKwWO3Q6HeLi4viqpNzcXBARysvLcePGDT6aDShTQt9//z1/XP5xnZmZswhV3YbGhirUxT6324MOPhpcokhlsKk0i9nBVx6xdIfFu5XC5l39I185r4bb7fETWDW4kZFXtOUpE/a1PFI2m2w8bVZYWIhNmzbBbLIpDO/l0OoFPnv2LP/0cb+Hdh5ACgHua4rpnvvHVatW8cJdtqEc169fV/gcvPHGGyAiRTqCYebMmfw8M2bM0OzAYOLICoNOhwt2u5O3oTFXNub5wNIG8nOpPXYLr0kjwayAx4twmVWoqWqRuhqsTui9kW1n/gztdp+JjlaawOl0odVo4/l2tUm9phua01/sa2pqsG/fPsyaNQvjx4/326LS1NSEzMxMNDY2Kh7PysoSheeeUQiw4MAhS0dkG8r5+nW5CK1duxbvv/8+0tPT/UTlvffew9ChQ6HT6QD4m82wli5WkGNjw1LxzJcLlroapFSAzepUrBbSnyjGpZxqNNZbeATKxn7lPc7MrwHwReBseajW0ATLG1/M9NlTBtqLp46US0t8nsBseEVRgNPolKioqFC8YbHXnq0hYkhJScHatWuRkZHBH6uursaHH36IqKiooP97GQAUAiw4sMgKc+piXE+hNU0nRZZV3pXwt7lrmsXk4KKlbjXzrRBSipvd3oHysiZ+bnnUycRWfT+BOhZ864icfi108nOnnZRMgthKos76gANNyKk/GRw/fhzz5s3DsGHDEBoaiqVLl3KjJ4YdO3bwTyLMS7ilpQUbNmzA3LlzxSfCwBQCLDjwqDZ2ByQHt6FDh2LBggVcGFpbW/HDDz8EFCEt4SstrsfJowXocLpRUyXtXMvLqVYU6+TDDmxFEIM8Gna7PXx9EXuOjXkHqyJPPlCRUYnbt28rxNBu70COoQJ2WyfOZV5BttscfIJPLabdGVbpziSdXq/HzZs3/e8huRjGFhO+/PJL5Obm8sdTjxeK4pw2NQU4bN26ddRdrF+/vvsHCwj0Aerq6igz00ChERaqrq6k5557jpYsWUKtra108OBBIiI6c+YMTZkyhaZOnUqOdhed05fSuOgR1NzSROFhg+jsqVIaN2EE5efV0d4dv9PQYYNo0l/HUNSQQVR3q40mxIyifx0dRQBozKNDacyjQ6jhjoWefm4cRQ6OoLueu/T3bTnkdt2lSU+M4fc2bsIIGjpsEMXO/gtZTE4qKbhDdTUmCgsPpRdmTKTQ0BAqu9FEf/uPf6Pw8FD+vNGPDKE7dSbSTZ9II0cOJyKic/pSOrjnCg0bHkl/mzKWIgdHKJ7DEB4RRpOeGEPhEWEUERFO4yaMpNDQUP78IUP/hf7y10cUfy8pvUi7d+8mo9FIkydPprCwMOn+o0fQsBGRNOO/JlF4RJjm6z9p0iQaNWqU4jF+ryMGk+tuM/3+++/U2tpKt6tC6R/7r9HwkYMpNMJC2dnZ9/bLf7BwZ926dbv8HtVS5UCk4L+LCD7EzM7OxpUrV0BEfEPJxx9/3GlUJ59wYz23XUWF6z89hRtF9aipakFGRoZmRwAgFd0Krv6BwnxpEwbzalD36cqdzBRjwLLIM9A1ugv18+WRu6nNoohIR44cibi4OMXGbDXq6uqwadMmHDp0CNXV1QGv5fAOv5jaLGg1mkV7WmCKFITgwOWYMWPg8XjwxRdfYOLEiZqiUVtbqylEXYmun7h4J946y6sajcaA+Vl598HFrCq+0l6xdLQXYltfX8+/bm1t9ft+VlYWdu3ahczMTAC+MeWuBibGjx/fqRGPy+VCfn4+du/ejQ0bNiiEWz6UkZSUJHLAgSkEWHDgMj4+HoC0qVeN7uQ7/Z7jNaO/V8in2uTFPva13Ke3J28EWkhMTOQGOQznzp1DU5NUAPR4PFi6dClCQkJw+fJl2G3OHkWkYWFhWLhwIVJSUuDxePjrajbZkJOTw68DAE6nE06ncvhl9erVQoADUwiw4MBlVlZWQGHqiS0jAJSXl2PlypUAoLlWpztg4qT2I2ZgQx5Wqx2mNisuZlT4rQvqKd58803cvHkTBw4cQFJSEo/u29qkjdcNDZKfcmZmJpYtWwYA+PXXX3sliJs3b+Z92PIIOjo6GosWLcKmTZtw5swZ3gnBXpNUkYIIRCHAggOPs2fPVoivPNq9evUq2traNNvM1Pjxxx/x7rvvApBcv1wuF2y27hmUa0XY8va0xgaL37Xl3rk1NTVwOru22ewK8+fPR0VFBfbv36/Yr5d6XEoJLF68GO+88w5aWlp4hF9bW4s1a9b0yie6O3vcQkJCEBMTg0OHDmkKtiCnEGDBgcUXXniBrwhi4qf39gTrTxR1KZROpxPbt2+Hw+HAkCFDoNfr0dTUhLKyMjgcjoCRs3rqS+u4dnsHHwXWmlST54crKiqwbds2/PTTTwHFVX7vaic2hrlz56Kqqgp79+5VCmSLtJuO7Sp89NFHcfToUX7e1H/2rjWsJ5Oe3RXsh5hCgAUHFletWoXUf/q2KB8+fFjaJadRwJIvx0w5XoC8vDwYjUbJazgtDY899pifFaRW58H06dO1xTFA0YyNH/PimoZwzpgxA998801A8QWUIs9GiQOlVAoLC/0sPcvKynD48GHF6/fzzz//qc5lYjS/UwoBFhxYZFEVEz+Px+OvRpCKU3FxcYoIjOV4AcmcR92upoVp06Zhz549XR4HqFa3s91tmYHHhgFfZJ2YmOhXBNRq7Wq3d+Ds2bP8PPL2NovZjtdff52/VidPnkR+fr7m6yei0n5BIcCCA4s6nQ5GozGgCC5atMjveBaBRUVF8eOio6PR3NzM/97c3KwQR3VnAYPZbFaInxz6AGkJfXIx2tqsAJRR7ZEjR1BZWYlr164hNDQUo0aN0rzm4sWLubOb0+nEzp07+Sj2/xXdUSzm9Hg8OHbsGF588UVs2bIFDocDq1evVgitiEr7DYUAC/ZvMrFYvnw5zp07pxCm2tpalJaWAgCKi4sxceLELs/H8Pbbb/OvbTYbvv32W5lfg38umWH//v18MIOZ8thtTmzbtg0tzW3QnyhSpBtYL+2lS5e4ILOolo1Jz5o1i9/fpEmT/K6ZmpqKt956yzvUUYRWoxlWq+SLbDJZ+bCDscWEffv28TcWi8UiuhD6N4UAC/ZfsrVE8l5Z5kHQ1NSEjRs3AgDOnDmD4cOHd3k+eYQpj3ZfeeUVP3GUIzEx0WvxWIS2Vgs3OlfnUnU6HY4dO8YLZjarA4mJibBZA3dWpKam+t3nkiVL/FIVycnJvMMh5XgBLl++DLfb7Zf3JSIMHjwYS5YsQUOD/z0G+3cqqKCmAIeTgEA/QGxsLF3MuEWH910lEKiyzkDfffcdVVRU0KBBg+izzz6jXbt20bJly8jtdnd5vnnz5vGvb1UZ6fcLNeS5e5fS09OJiChycATFLXxS8ZyZM2fS1KlTyZB+kw7vu0qeu3dp/mtP0eQnH6HHxkaR2+Oh56dH08b/NVBubi7l5eXR2DFTyGxyUv7lOnrttdfIkH6T/rH/GlFICLlCKujll1+mqKgoIiL65Zdf/O5z5MiR9FtaKX9O3MInKS4ujtrtHQQCPT89ms79lk4TJ06k3NxcIiL+JxFRe3s77dy5kwoKCig1JU1xjwIDACICFuwP1CoYbd26FYA04fXJJ5/06HxHjhxBY2Mjb2PTWt0un/YymWw8wvStRZJyubW1tZq51ISEBO7/YLd14KuvvkJLcxtSjxei3d6BFStW4ODBg/waWqkB+fVsNge/t1u3biE9PR0JCQnYt28fqquru/Uainxvv6VmBOxvtyQgEATk5ubS/AVzKPvSQZq/YA4RESUk/A+dPllCFrOdLly40O1zvfTSS7Rw4UIaPnw4ZZ4tp4N7rlD2b5U0aJDvA19JSQnt2LGDDGdu0sE9VyjrXAXFxsby+7hVn02hoSF0+mQJDR8muYFt3rxZEX2OHj2aDKfL6NDeK3T+dCmZzWaav2AO1TXlUOTgCCooKKCnnnqKMrzXuJh5i2JjYwP+3G+9tZgaGxuJiGjM6MfIbRtLX2/+hiZMmEAWi6Vbr6H6HgX6OUQELNgfuWrVKqQcL+hxTpPlkqXOhQ5YLO3S1mRVTjYsLEyKPltMmm1a6kkzresHavOaNWsWAPBWOLPJ1u1WMJ1Oh46ODkU+t6ysDBcuXAj670TwniiKcIIDh73tYVUL97Fjx+B0uhTTavJdZoE+tnf3+lrPj4uLAyA5ptXV1SE2NrZHqYE1a9Z4fR6K0NLchqKiIqSlpQX9dyJ4TxQCLDiw2Jucplo4k5KSuEcBW7F+P69PRIpNHfHx8b362VlL2+7du1FcXIyjR48G/fcheE8UAiz4cFAunFKawTdNN23atPt+fZZ6KCsrQ2hoaK/PwzYUu1wu5OXlieLawKYQYMGHk0VF0rBFTk7Ofb+WTqdDW6sFacnFaGu13JNoRkRE4NKlS5LxjzcdIUR4wFJ0QQg8fJg+fTrFPD6ZTp8soScm/zvpdLr7er3Y2FjKPl8ldV6cr/LreugJXC4X6fV6MqSX0eG9VzW7KAQGNoQACzzQWLhwYadtYH0Ng8FAz0+Ppv9+9z/p+enRZDAY7ul8er2enntxXJ+dT6CfQaQgBB9k6nQ6GAO0mt3Pa/blQIQYsHggqJmCCPEKa7cQEhLS/YMFBPoJdDodxcbGksFgEEMKAsHCFQDPqB8UAiwgICBw/6EpwCIHLCAgIBAkCAEWEBAQCBKEAAsICAgECUKABQQEBIIEIcACAgICQYIQYAEBAYEgoacriZqJqOZ+3IiAgIDAA4wJWg/2qA9YQEBAQKDvIFIQAgICAkGCEGABAQGBIEEIsICAgECQIARYQEBAIEgQAiwgICAQJAgBFhAQEAgShAALCAgIBAlCgAUEBASCBCHAAgICAkHC/wNzjEArp1/lFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29ff7fcec08>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot with the specified dimensions.\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Colors uses a color map, which will produce an array of colors based on\n",
    "# the number of labels there are. We use set(k_means_labels) to get the\n",
    "# unique labels.\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "\n",
    "# Create a plot with a black background (background is black because we can see the points\n",
    "# connection to the centroid.\n",
    "ax = fig.add_subplot(1, 1, 1, facecolor = 'black')\n",
    "\n",
    "# For loop that plots the data points and centroids.\n",
    "# k will range from 0-3, which will match the possible clusters that each\n",
    "# data point is in.\n",
    "\n",
    "#print(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "#print('colors:',colors)\n",
    "#print(range(len([[2, 2], [-2, -1], [4, -3], [1, 1]])))\n",
    "\n",
    "\n",
    "for k, col in zip(range(len([[2, 2], [-2, -1], [4, -3], [1, 1]])), colors):\n",
    "\n",
    "    # Create a list of all data points, where the data points that are \n",
    "    # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
    "    # labeled as false.\n",
    "    my_members = (k_means_labels == k)\n",
    "    \n",
    "    # Define the centroid, or cluster center.\n",
    "    cluster_center = k_means_cluster_centers[k]\n",
    "    \n",
    "    # Plots the datapoints with color col. 'w'- white\n",
    "    #https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.axes.Axes.plot.html \n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w',\n",
    "            markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plots the centroids with specified color, but with a darker outline\n",
    "    #'o' circle marker; 'k' - black\n",
    "    #for detail of format string, https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.axes.Axes.plot.html \n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n",
    "            markeredgecolor='k', markersize=6)\n",
    "\n",
    "# Title of the plot\n",
    "ax.set_title('KMeans')\n",
    "\n",
    "# Remove x-axis ticks\n",
    "ax.set_xticks(())\n",
    "\n",
    "# Remove y-axis ticks\n",
    "ax.set_yticks(())\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Display the scatter plot from above for comparison.\n",
    "plt.scatter(X[:, 0], X[:, 1], marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a id = \"iris\"></a>\n",
    "\n",
    "\n",
    "### <span style=\"color:#0b486b\">1.4 Clustering Iris Data</span>\n",
    "\n",
    "Import the following libraries: \n",
    "<ol>- Axes3D from mpl_toolkits.mplot3d</ol>\n",
    "<ol>- KMeans from sklearn.cluster</ol>\n",
    "<ol>- load_iris from sklearn.datasets</ol>\n",
    "\n",
    "<i>Note: It is presumed that numpy and matplotlib.pyplot are both imported as np and plt respectively from previous imports. If that is not the case, please import them!</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "from sklearn.cluster import KMeans \n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will set the <b>random seed</b> and the <b>centers</b> for <b>K-means</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(5)\n",
    "\n",
    "centers = [[1, 1], [-1, -1], [1, -1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the <b> load_iris() </b> function, declare the iris datset as the variable <b>iris</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also declare <b>X</b> as the <b>iris' data component</b>, and y as <b>iris' target component</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data \n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's run the rest of the code and see what <b>K-Means produces!</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "estimators = {'k_means_iris_3': KMeans(n_clusters=3),\n",
    "              'k_means_iris_8': KMeans(n_clusters=8),\n",
    "              'k_means_iris_bad_init': KMeans(n_clusters=3, n_init=1,\n",
    "                                              init='random')}\n",
    "\n",
    "fignum = 1\n",
    "for name, est in estimators.items():\n",
    "    fig = plt.figure(fignum, figsize=(4, 3))\n",
    "    \n",
    "    #plt.clf() clear the current figure\n",
    "    #plt. clf() clears the entire current figure with all its axes, \n",
    "    #but leaves the window opened, such that it may be reused for other plots. \n",
    "    plt.clf()\n",
    "    \n",
    "    #https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html\n",
    "    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "\n",
    "    # plt.cla() clear the current axes\n",
    "    plt.cla()\n",
    "    est.fit(X)\n",
    "    labels = est.labels_\n",
    "\n",
    "    ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(np.float))\n",
    "\n",
    "    ax.w_xaxis.set_ticklabels([])\n",
    "    ax.w_yaxis.set_ticklabels([])\n",
    "    ax.w_zaxis.set_ticklabels([])\n",
    "    ax.set_xlabel('Petal width')\n",
    "    ax.set_ylabel('Sepal length')\n",
    "    ax.set_zlabel('Petal length')\n",
    "    fignum = fignum + 1\n",
    "\n",
    "# Plot the ground truth\n",
    "fig = plt.figure(fignum, figsize=(4, 3))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "\n",
    "plt.cla()\n",
    "\n",
    "for name, label in [('Setosa', 0),\n",
    "                    ('Versicolour', 1),\n",
    "                    ('Virginica', 2)]:\n",
    "    ax.text3D(X[y == label, 3].mean(),\n",
    "              X[y == label, 0].mean() + 1.5,\n",
    "              X[y == label, 2].mean(), name,\n",
    "              horizontalalignment='center',\n",
    "              bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))\n",
    "# Reorder the labels to have colors matching the cluster results\n",
    "y = np.choose(y, [1, 2, 0]).astype(np.float)\n",
    "ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y)\n",
    "\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "ax.set_xlabel('Petal width')\n",
    "ax.set_ylabel('Sepal length')\n",
    "ax.set_zlabel('Petal length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following <b>plots</b> (1-3) show the different <b>end results</b> you obtain by using different <b>initalization processes</b>. <b>Plot 4</b> holds what the answer should be, however it is clear that <b>K-means</b> is <b>heavily reliant</b> on the <b>initalization</b> of the <b>centroid</b>.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## <span style=\"color:#0b486b\">2. Hierarchical Clustering</span>\n",
    "\n",
    "\n",
    "We will be looking at the next clustering technique, which is <b>Agglomerative Hierarchical Clustering, which is more popular than Divisive clustering.</b>. Remember that agglomerative is the bottom up approach. <br> <br>\n",
    "\n",
    "We will also be using Complete Linkage as the Linkage Criteria. <br>\n",
    "<b> <i> NOTE: You can also try using Average Linkage wherever Complete Linkage would be used to see the difference! </i> </b>\n",
    "\n",
    "---\n",
    "Import Libraries:\n",
    "<ul>\n",
    "    <li> <b>numpy as np</b> </li>\n",
    "    <li> <b>ndimage</b> from <b>scipy</b> </li>\n",
    "    <li> <b>hierarchy</b> from <b>scipy.cluster</b> </li>\n",
    "    <li> <b>pyplot as plt</b> from <b>matplotlib</b> </li>\n",
    "    <li> <b>manifold</b> from <b>sklearn</b> </li>\n",
    "    <li> <b>datasets</b> from <b>sklearn</b> </li>\n",
    "    <li> <b>AgglomerativeClustering</b> from <b>sklearn</b> </li>\n",
    "    <li> <b>make_blobs</b> from <b>sklearn.datasets.samples_generator</b> </li>\n",
    "</ul> <br>\n",
    "Also run <b>%matplotlib inline</b> that that wasn't run already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from scipy import ndimage \n",
    "from scipy.cluster import hierarchy \n",
    "from scipy.spatial import distance_matrix \n",
    "from matplotlib import pyplot as plt \n",
    "from sklearn import manifold, datasets \n",
    "from sklearn.cluster import AgglomerativeClustering \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"data2\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">2.1 Generating Random Data</span>\n",
    "\n",
    "We will be generating another set of data using the <b>make_blobs</b> class once again. This time you will input your own values! <br> <br>\n",
    "Input these parameters into make_blobs:\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Choose a number from 10-1500 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Choose arrays of x,y coordinates for generating the centers. Have 1-10 centers (ex. centers=[[1,1], [2,5]]) </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. The larger the number, the further apart the clusters</li>\n",
    "    <ul> <li> Choose a number between 0.5-1.5 </li> </ul>\n",
    "</ul> <br>\n",
    "Save the result to <b>X2</b> and <b>y2</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2, y2 = make_blobs(n_samples=50, centers=[[4,4], [-2, -1], [1, 1], [10,4]], cluster_std=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scatter plot of the randomly generated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29ff87bf988>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X2[:, 0], X2[:, 1], marker='.') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id = \"agc\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">2.2 Agglomerative Clustering</span>\n",
    "\n",
    "We will start by clustering the random data points we just created.\n",
    "\n",
    "The <b> AgglomerativeClustering </b> class will require two inputs:\n",
    "<ul>\n",
    "    <li> <b>n_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
    "    <ul> <li> Value will be: 4 </li> </ul>\n",
    "    <li> <b>linkage</b>: Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion. </li>\n",
    "    <ul> \n",
    "        <li> Value will be: 'complete' </li> \n",
    "        <li> <b>Note</b>: It is recommended you try everything with 'average' as well </li>\n",
    "    </ul>\n",
    "</ul> <br>\n",
    "Save the result to a variable called <b> agglom </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "agglom = AgglomerativeClustering(n_clusters = 4, linkage = 'average')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model with <b> X2 </b> and <b> y2 </b> from the generated data above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "                        connectivity=None, distance_threshold=None,\n",
       "                        linkage='average', memory=None, n_clusters=4,\n",
       "                        pooling_func='deprecated')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglom.fit(X2,y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to show the clustering! <br>\n",
    "Remember to read the code and comments to gain more understanding on how the plotting works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29ff88d6d88>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure of size 6 inches by 4 inches.\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "# These two lines of code are used to scale the data points down,\n",
    "# Or else the data points will be scattered very far apart.\n",
    "\n",
    "# Create a minimum and maximum range of X2.\n",
    "x_min, x_max = np.min(X2, axis=0), np.max(X2, axis=0)\n",
    "\n",
    "# Get the average distance for X2.\n",
    "X2 = (X2 - x_min) / (x_max - x_min)\n",
    "\n",
    "# This loop displays all of the datapoints.\n",
    "#added by Shang\n",
    "cmap = plt.cm.get_cmap(\"Spectral\")\n",
    "\n",
    "for i in range(X2.shape[0]):\n",
    "    # Replace the data points with their respective cluster value \n",
    "    # (ex. 0) and is color coded with a colormap (plt.cm.spectral)\n",
    "    #\n",
    "    #\"spectral usage: added by Shang\n",
    "    #cmap = plt.cm.get_cmap(\"Spectral\")\n",
    "    #colors = cmap(a / b)\n",
    "    #\n",
    "    \n",
    "    #plt.text(X2[i, 0], X2[i, 1], str(y2[i]),\n",
    "    #         color=plt.cm.spectral(agglom.labels_[i] / 10.),\n",
    "    #         fontdict={'weight': 'bold', 'size': 9})\n",
    "        \n",
    "    plt.text(X2[i, 0], X2[i, 1], str(y2[i]),\n",
    "             color=cmap(agglom.labels_[i] / 10.), \n",
    "             fontdict={'weight': 'bold', 'size': 9})\n",
    "    \n",
    "# Remove the x ticks, y ticks, x and y axis\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.axis('off')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n",
    "\n",
    "# Display the plot of the original data before clustering\n",
    "plt.scatter(X2[:, 0], X2[:, 1], marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id = \"den\"></a>\n",
    "\n",
    "### <span style=\"color:#0b486b\">2.3 Dendrogram</span>\n",
    "\n",
    "\n",
    "Remember that a <b>distance matrix</b> contains the <b> distance from each point to every other point of a dataset </b>. <br>\n",
    "Use the function <b> distance_matrix, </b> which requires <b>two inputs</b>. Use the Feature Matrix, <b> X2 </b> as both inputs and save the distance matrix to a variable called <b> dist_matrix </b> <br> <br>\n",
    "Remember that the distance values are symmetric, with a diagonal of 0's. This is one way of making sure your matrix is correct. <br> (print out dist_matrix to make sure it's correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.08750199 0.12373912 ... 0.72939926 0.14221722 0.12149305]\n",
      " [0.08750199 0.         0.17893395 ... 0.73549091 0.13252706 0.20675124]\n",
      " [0.12373912 0.17893395 0.         ... 0.84295371 0.11197173 0.16143366]\n",
      " ...\n",
      " [0.72939926 0.73549091 0.84295371 ... 0.         0.8613293  0.70226941]\n",
      " [0.14221722 0.13252706 0.11197173 ... 0.8613293  0.         0.24061935]\n",
      " [0.12149305 0.20675124 0.16143366 ... 0.70226941 0.24061935 0.        ]]\n",
      "\n",
      "[0.08750199 0.12373912 0.25732255 ... 0.8613293  0.70226941 0.24061935]\n",
      "\n",
      "[0.08750199 0.12373912 0.25732255 ... 0.8613293  0.70226941 0.24061935]\n"
     ]
    }
   ],
   "source": [
    "dist_matrix = distance_matrix(X2,X2) \n",
    "print(dist_matrix)\n",
    "#condense the distance matrix using hierarchy.distance.pdisk \n",
    "#you should see the condensed distance matrix is a flat array. \n",
    "#It is the upper triangular of the distance matrix.\n",
    "condensed_dist_matrix= hierarchy.distance.pdist(X2,'euclidean')\n",
    "print()\n",
    "print(condensed_dist_matrix)\n",
    "\n",
    "#the following is another way to produce condensed_dist_matrix\n",
    "#import scipy.spatial.distance as ssd\n",
    "## convert the redundant n*n square matrix form into a condensed nC2 array     \n",
    "## distArray[{n choose 2}-{n-i choose 2} + (j-i-1)] is the distance between points i and j\n",
    "##https://stackoverflow.com/questions/18952587/use-distance-matrix-in-scipy-cluster-hierarchy-linkage\n",
    "#condensed_dist_matrix = ssd.squareform(dist_matrix)\n",
    "#print()\n",
    "#print(condensed_dist_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the <b> linkage </b> class from hierarchy, pass in the parameters:\n",
    "<ul>\n",
    "    <li> The condensed distance matrix </li>\n",
    "    <li> 'complete' for complete linkage </li>\n",
    "</ul> <br>\n",
    "Save the result to a variable called <b> Z </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = hierarchy.linkage(condensed_dist_matrix, 'complete')\n",
    "#Z = hierarchy.linkage(X2, 'complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will save the dendrogram to a variable called <b>dendro</b>. In doing this, the dendrogram will also be displayed.\n",
    "Using the <b> dendrogram </b> class from hierarchy, pass in the parameter:\n",
    "<ul> <li> Z </li> </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dendro = hierarchy.dendrogram(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## <span style=\"color:#0b486b\">3. Density-based Clustering</span>\n",
    "\n",
    "\n",
    "We will be looking at the next clustering technique, which is <b>Agglomerative Hierarchical Clustering</b>. Remember that agglomerative is the bottom up approach. <br> <br>\n",
    "In this lab, we will be looking at Agglomerative clustering, which is more popular than Divisive clustering. <br> <br>\n",
    "We will also be using Complete Linkage as the Linkage Criteria. <br>\n",
    "<b> <i> NOTE: You can also try using Average Linkage wherever Complete Linkage would be used to see the difference! </i> </b>\n",
    "\n",
    "---\n",
    "Import Libraries:\n",
    "<ul>\n",
    "    <li> <b>numpy as np</b> </li>\n",
    "    <li> <b>ndimage</b> from <b>scipy</b> </li>\n",
    "    <li> <b>hierarchy</b> from <b>scipy.cluster</b> </li>\n",
    "    <li> <b>pyplot as plt</b> from <b>matplotlib</b> </li>\n",
    "    <li> <b>manifold</b> from <b>sklearn</b> </li>\n",
    "    <li> <b>datasets</b> from <b>sklearn</b> </li>\n",
    "    <li> <b>AgglomerativeClustering</b> from <b>sklearn</b> </li>\n",
    "    <li> <b>make_blobs</b> from <b>sklearn.datasets.samples_generator</b> </li>\n",
    "</ul> <br>\n",
    "Also run <b>%matplotlib inline</b> that that wasn't run already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from sklearn.cluster import DBSCAN \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below will generate the data points and requires these inputs:\n",
    "<ul>\n",
    "    <li> <b>centroidLocation</b>: Coordinates of the centroids that will generate the random data. </li>\n",
    "    <ul> <li> Example: input: [[4,3], [2,-1], [-1,4]] </li> </ul>\n",
    "    <li> <b>numSamples</b>: The number of data points we want generated, split over the number of centroids (# of centroids defined in centroidLocation) </li>\n",
    "    <ul> <li> Example: 1500 </li> </ul>\n",
    "    <li> <b>clusterDeviation</b>: The standard deviation between the clusters. The larger the number, the further the spacing. </li>\n",
    "    <ul> <li> Example: 0.5 </li> </ul>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createDataPoints(centroidLocation, numSamples, clusterDeviation):\n",
    "    # Create random data and store in feature matrix X and response vector y.\n",
    "    X, y = make_blobs(n_samples=numSamples, centers=centroidLocation, \n",
    "                                cluster_std=clusterDeviation)\n",
    "    \n",
    "    # Standardize features by removing the mean and scaling to unit variance\n",
    "    X = StandardScaler().fit_transform(X)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below will generate the DBSCAN using the input data:\n",
    "<ul>\n",
    "    <li> <b>epsilon</b>: A float that describes the maximum distance between two samples for them to be considered as in the same neighborhood. </li>\n",
    "    <ul> <li> Example: 0.3 </li> </ul>\n",
    "    <li> <b>minimumSamples</b>: The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. </li>\n",
    "    <ul> <li> Examples: 7 </li> </ul>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def displayDBSCAN(epsilon, minimumSamples):\n",
    "    \n",
    "    # Initialize DBSCAN with specified epsilon and min. smaples. Fit the model with feature\n",
    "    # matrix X\n",
    "    db = DBSCAN(eps=epsilon, min_samples=minimumSamples).fit(X)\n",
    "    \n",
    "    # Create an array of booleans using the labels from db.\n",
    "    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "    \n",
    "    # Replace all elements with 'True' in core_samples_mask that are\n",
    "    # in the cluster, 'False' if the points are outliers.\n",
    "    core_samples_mask[db.core_sample_indices_] = True\n",
    "    labels = db.labels_\n",
    "\n",
    "    # Number of clusters in labels, ignoring noise if present.\n",
    "    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "\n",
    "\n",
    "    # Black color is removed and used for noise instead.\n",
    "    \n",
    "    # Remove repetition in labels by turning it into a set.\n",
    "    unique_labels = set(labels)\n",
    "    \n",
    "    # Create colors for the clusters.\n",
    "    colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))\n",
    "    \n",
    "    # Plot the points with colors\n",
    "    for k, col in zip(unique_labels, colors):\n",
    "        if k == -1:\n",
    "            # Black used for noise.\n",
    "            col = 'k'\n",
    "\n",
    "        class_member_mask = (labels == k)\n",
    "        \n",
    "        # Plot the datapoints that are clustered\n",
    "        xy = X[class_member_mask & core_samples_mask]\n",
    "        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n",
    "                 markeredgecolor='k', markersize=14)\n",
    "\n",
    "        # Plot the outliers\n",
    "        xy = X[class_member_mask & ~core_samples_mask]\n",
    "        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n",
    "                 markeredgecolor='k', markersize=6)\n",
    "\n",
    "    plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use <b>createDataPoints</b> with the <b>3 inputs</b> and store the output into variables <b>X</b> and <b>y</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-1.20481012,  0.8947502 ],\n",
       "        [-1.33347483,  0.64553883],\n",
       "        [ 0.63510302, -1.77670748],\n",
       "        ...,\n",
       "        [ 0.18916549, -1.41081505],\n",
       "        [-1.11560064,  0.80583478],\n",
       "        [-0.20255851, -1.16925803]]), array([2, 2, 1, ..., 1, 2, 1]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "createDataPoints([[4,3], [2,-1], [-1,4]] , 1500, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "displayDBSCAN(0.3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:#0b486b\">Tasks</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may wish to <b>change</b> the values of <b>elev</b> and <b>azim</b> if you would like to view the graph in <b>different perspectives</b>. <b>Elev</b> controls the <b>elevation</b> of the <b>z plane</b> and <b>azim</b> controls the <b>azimuth angle</b> in the <b>x,y plane</b>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try the provided examples and get yourself familiar with sample code before attempting portolio tasks.\n",
    "\n",
    "Please show your attempt to your tutor before you leave the lab, or email your files to your coordinator if you are an off-campus student."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:#0b486b\">Summary</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this session we have covered: \n",
    " - different unsupervised learning algorithms.\n",
    " - how to apply the unsupervised learning algorithms in Python."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
